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Samsung confirms next generation HBM4 memory is in fact Snowbolt — and reveals it plans to flood the market with precious AI memory amidst growing competition with SK Hynix and Micron

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Samsung has revealed it expects to triple its HBM chip production this year.

“Following the third-generation HBM2E and fourth-generation HBM3, which are already in mass production, we plan to produce the 12-layer fifth-generation HBM and 32 gigabit-based 128 GB DDR5 products in large quantities in the first half of the year,” SangJoon Hwang, EVP and Head of DRAM Product and Technology Team at Samsung said during a speech at Memcon 2024.

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Dragon’s Dogma 2 players can now enable DLSS 3 and Frame Generation thanks to this handy mod

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Some Dragon’s Dogma 2 players with an RTX 40-series graphics card have been experiencing performance issues on PC, but this new mod might offer some improvements.

Before the release of Capcom’s open-world fantasy RPG, it was said that the Dragon’s Dogma 2 would be released with DLSS 3, however, as players come to find out, that wasn’t the case. Strangely enough, the game installation folder does feature the DLSS 3 file for Frame Generation but is currently inaccessible for owners of an RTX 40-series GPU.

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“The world’s most powerful chip” — Nvidia says its new Blackwell is set to power the next generation of AI

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The next generation of AI will be powered by Nvidia hardware, the company has declared after it revealed its next generation of GPUs.

Company CEO Jensen Huang took the wraps off the new Blackwell chips at Nvidia GTC 2024 today, promising a major step forward in terms of AI power and efficiency.

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Integrated optical frequency division for microwave and mmWave generation

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Microwave and mmWave with high spectral purity are critical for a wide range of applications1,2,3, including metrology, navigation and spectroscopy. Owing to the superior fractional frequency stability of reference-cavity stabilized lasers when compared to electrical oscillators14, the most stable microwave sources are now achieved in optical systems by using optical frequency division4,5,6,7 (OFD). Essential to the division process is an optical frequency comb4, which coherently transfers the fractional stability of stable references at optical frequencies to the comb repetition rate at radio frequency. In the frequency division, the phase noise of the output signal is reduced by the square of the division ratio relative to that of the input signal. A phase noise reduction factor as large as 86 dB has been reported4. However, so far, the most stable microwaves derived from OFD rely on bulk or fibre-based optical references4,5,6,7, limiting the progress of applications that demand exceedingly low microwave phase noise.

Integrated photonic microwave oscillators have been studied intensively for their potential of miniaturization and mass-volume fabrication. A variety of photonic approaches have been shown to generate stable microwave and/or mmWave signals, such as direct heterodyne detection of a pair of lasers15, microcavity-based stimulated Brillouin lasers16,17 and soliton microresonator-based frequency combs18,19,20,21,22,23 (microcombs). For solid-state photonic oscillators, the fractional stability is ultimately limited by thermorefractive noise (TRN), which decreases with the increase of cavity mode volume24. Large-mode-volume integrated cavities with metre-scale length and a greater than 100 million quality (Q)-factor have been shown recently8,25 to reduce laser linewidth to Hz-level while maintaining chip footprint at centimetre-scale9,26,27. However, increasing cavity mode volume reduces the effective intracavity nonlinearity strength and increases the turn-on power for Brillouin and Kerr parametric oscillation. This trade-off poses a difficult challenge for an integrated cavity to simultaneously achieve high stability and nonlinear oscillation for microwave generation. For oscillators integrated with photonic circuits, the best phase noise reported at 10 kHz offset frequency is demonstrated in the SiN photonic platform, reaching −109 dBc Hz−1 when the carrier frequency is scaled to 10 GHz (refs. 21,26). This is many orders of magnitude higher than that of the bulk OFD oscillators. An integrated photonic version of OFD can fundamentally resolve this trade-off, as it allows the use of two distinct integrated resonators in OFD for different purposes: a large-mode-volume resonator to provide exceptional fractional stability and a microresonator for the generation of soliton microcombs. Together, they can provide major improvements to the stability of integrated oscillators.

Here, we notably advance the state of the art in photonic microwave and mmWave oscillators by demonstrating integrated chip-scale OFD. Our demonstration is based on complementary metal-oxide-semiconductor-compatible SiN integrated photonic platform28 and reaches record-low phase noise for integrated photonic-based mmWave oscillator systems. The oscillator derives its stability from a pair of commercial semiconductor lasers that are frequency stabilized to a planar-waveguide-based reference cavity9 (Fig. 1). The frequency difference of the two reference lasers is then divided down to mmWave with a two-point locking method29 using an integrated soliton microcomb10,11,12. Whereas stabilizing soliton microcombs to long-fibre-based optical references has been shown very recently30,31, its combination with integrated optical references has not been reported. The small dimension of microcavities allows soliton repetition rates to reach mmWave and THz frequencies12,30,32, which have emerging applications in 5G/6G wireless communications33, radio astronomy34 and radar2. Low-noise, high-power mmWaves are generated by photomixing the OFD soliton microcombs on a high-speed flip-chip bonded charge-compensated modified uni-travelling carrier photodiode (CC-MUTC PD)12,35. To address the challenge of phase noise characterization for high-frequency signals, a new mmWave to microwave frequency division (mmFD) method is developed to measure mmWave phase noise electrically while outputting a low-noise auxiliary microwave signal. The generated 100 GHz signal reaches a phase noise of −114 dBc Hz−1 at 10 kHz offset frequency (equivalent to −134 dBc Hz−1 for 10 GHz carrier frequency), which is more than two orders of magnitude better than previous SiN-based photonic microwave and mmWave oscillators21,26. The ultra-low phase noise can be maintained while pushing the mmWave output power to 9 dBm (8 mW), which is only 1 dB below the record for photonic oscillators at 100 GHz (ref. 36). Pictures of chip-based reference cavity, soliton-generating microresonators and CC-MUTC PD are shown in Fig. 1b.

Fig. 1: Conceptual illustration of integrated OFD.
figure 1

a, Simplified schematic. A pair of lasers that are stabilized to an integrated coil reference cavity serve as the optical references and provide phase stability for the mmWave and microwave oscillator. The relative frequency difference of the two reference lasers is then divided down to the repetition rate of a soliton microcomb by feedback control of the frequency of the laser that pumps the soliton. A high-power, low-noise mmWave is generated by photodetecting the OFD soliton microcomb on a CC-MUTC PD. The mmWave can be further divided down to microwave through a mmWave to microwave frequency division with a division ratio of M. PLL, phase lock loop. b, Photograph of critical elements in the integrated OFD. From left to right are: a SiN 4 m long coil waveguide cavity as an optical reference, a SiN chip with tens of waveguide-coupled ring microresonators to generate soliton microcombs, a flip-chip bonded CC-MUTC PD for mmWave generation and a US 1-cent coin for size comparison. Microscopic pictures of a SiN ring resonator and a CC-MUTC PD are shown on the right. Scale bars, 100 μm (top and bottom left), 50 μm (bottom right).

The integrated optical reference in our demonstration is a thin-film SiN 4-metre-long coil cavity9. The cavity has a cross-section of 6 μm width × 80 nm height, a free-spectral-range (FSR) of roughly 50 MHz, an intrinsic quality factor of 41 × 106 (41 × 106) and a loaded quality factor of 34 × 106 (31 × 106) at 1,550 nm (1,600 nm). The coil cavity provides exceptional stability for reference lasers because of its large-mode volume and high-quality factor9. Here, two widely tuneable lasers (NewFocus Velocity TLB-6700, referred to as laser A and B) are frequency stabilized to the coil cavity through Pound–Drever–Hall locking technique with a servo bandwidth of 90 kHz. Their wavelengths can be tuned between 1,550 nm (fA = 193.4 THz) and 1,600 nm (fB = 187.4 THz), providing up to 6 THz frequency separation for OFD. The setup schematic is shown in Fig. 2.

Fig. 2: Experimental setup.
figure 2

A pair of reference lasers is created by stabilizing frequencies of lasers A and B to a SiN coil waveguide reference cavity, which is temperature controlled by a thermoelectric cooler (TEC). Soliton microcomb is generated in an integrated SiN microresonator. The pump laser is the first modulation sideband of a modulated continuous wave laser, and the sideband frequency can be rapidly tuned by a VCO. To implement two-point locking for OFD, the 0th comb line (pump laser) is photomixed with reference laser A, while the –Nth comb line is photomixed with reference laser B. The two photocurrents are then subtracted on an electrical mixer to yield the phase difference between the reference lasers and N times the soliton repetition rate, which is then used to servo control the soliton repetition rate by controlling the frequency of the pump laser. The phase noise of the reference lasers and the soliton repetition rate can be measured in the optical domain by using dual-tone delayed self-heterodyne interferometry. Low-noise, high-power mmWaves are generated by detecting soliton microcombs on a CC-MUTC PD. To characterize the mmWave phase noise, a mmWave to  microwave frequency division is implemented to stabilize a 20 GHz VCO to the 100 GHz mmWave and the phase noise of the VCO can be directly measured by a phase noise analyser (PNA). Erbium-doped fibre amplifiers (EDFAs), polarization controllers (PCs), phase modulators (PMs), single-sideband modulator (SSB-SC), band pass filters (BPFs), fibre-Bragg grating (FBG) filters, line-by-line waveshaper (WS), acoustic-optics modulator (AOM), electrical amplifiers (Amps) and a source meter (SM) are also used in the experiment.

The soliton microcomb is generated in an integrated, bus-waveguide-coupled Si3N4 micro-ring resonator10,12 with a cross-section of 1.55 μm width × 0.8 μm height. The ring resonator has a radius of 228 μm, an FSR of 100 GHz and an intrinsic (loaded) quality factor of 4.3 × 106 (3.0 × 106). The pump laser of the ring resonator is derived from the first modulation sideband of an ultra-low-noise semiconductor extended distributed Bragg reflector laser from Morton Photonics37, and the sideband frequency can be rapidly tuned by a voltage-controlled oscillator (VCO). This allows single soliton generation by implementing rapid frequency sweeping of the pump laser38, as well as fast servo control of the soliton repetition rate by tuning the VCO30. The optical spectrum of the soliton microcombs is shown in Fig. 3a, which has a 3 dB bandwidth of 4.6 THz. The spectra of reference lasers are also plotted in the same figure.

Fig. 3: OFD characterization.
figure 3

a, Optical spectra of soliton microcombs (blue) and reference (Ref.) lasers corresponding to different division ratios. b, Phase noise of the frequency difference between the two reference lasers stabilized to coil cavity (orange) and the two lasers at free running (blue). The black dashed line shows the thermal refractive noise (TRN) limit of the reference cavity. c, Phase noise of reference lasers (orange), the repetition rate of free-running soliton microcombs (light blue), soliton repetition rate after OFD with a division ratio of 60 (blue) and the projected repetition rate with 60 division ratio (red). d, Soliton repetition rate phase noise at 1 and 10 kHz offset frequencies versus OFD division ratio. The projections of OFD are shown with coloured dashed lines.

The OFD is implemented with the two-point locking method29,30. The two reference lasers are photomixed with the soliton microcomb on two separate photodiodes to create beat notes between the reference lasers and their nearest comb lines. The beat note frequencies are Δ1 = fA − (fp + n × fr) and Δ2 = fB − (fp + m × fr), where fr is the repetition rate of the soliton, fp is pump laser frequency and n, m are the comb line numbers relative to the pump line number. These two beat notes are then subtracted on an electrical mixer to yield the frequency and phase difference between the optical references and N times of the repetition rate: Δ = Δ1 − Δ2 = (fA − fB) − (N × fr), where N = n − m is the division ratio. Frequency Δ is then divided by five electronically and phase locked to a low-frequency local oscillator (LO, fLO1) by feedback control of the VCO frequency. The tuning of VCO frequency directly tunes the pump laser frequency, which then tunes the soliton repetition rate through Raman self-frequency shift and dispersive wave recoil effects20. Within the servo bandwidth, the frequency and phase of the optical references are thus divided down to the soliton repetition rate, as fr = (fA − fB − 5fLO1)/N. As the local oscillator frequency is in the 10 s MHz range and its phase noise is negligible compared to the optical references, the phase noise of the soliton repetition rate (Sr) within the servo locking bandwidth is determined by that of the optical references (So): Sr = So/N2.

To test the OFD, the phase noise of the OFD soliton repetition rate is measured for division ratios of N = 2, 3, 6, 10, 20, 30 and 60. In the measurement, one reference laser is kept at 1,550.1 nm, while the other reference laser is tuned to a wavelength that is N times of the microresonator FSR away from the first reference laser (Fig. 3a). The phase noise of the reference lasers and soliton microcombs are measured in the optical domain by using dual-tone delayed self-heterodyne interferometry39. In this method, two lasers at different frequencies can be sent into an unbalanced Mach–Zehnder interferometer with an acoustic-optics modulator in one arm (Fig. 2). Then the two lasers are separated by a fibre-Bragg grating filter and detected on two different photodiodes. The instantaneous frequency and phase fluctuations of these two lasers can be extracted from the photodetector signals by using Hilbert transform. Using this method, the phase noise of the phase difference between the two stabilized reference lasers is measured and shown in Fig. 3b. In this work, the phase noise of the reference lasers does not reach the thermal refractive noise limit of the reference cavity9 and is likely to be limited by environmental acoustic and mechanical noises. For soliton repetition rate phase noise measurement, a pair of comb lines with comb numbers l and k are selected by a programmable line-by-line waveshaper and sent into the interferometry. The phase noise of their phase differences is measured, and its division by (l − k)2 yields the soliton repetition rate phase noise39.

The phase noise measurement results are shown in Fig. 3c,d. The best phase noise for soliton repetition rate is achieved with a division ratio of 60 and is presented in Fig. 3c. For comparison, the phase noises of reference lasers and the repetition rate of free-running soliton without OFD are also shown in the figure. Below 100 kHz offset frequency, the phase noise of the OFD soliton is roughly 602, which is 36 dB below that of the reference lasers and matches very well with the projected phase noise for OFD (noise of reference lasers – 36 dB). From roughly 148 kHz (OFD servo bandwidth) to 600 kHz offset frequency, the phase noise of the OFD soliton is dominated by the servo pump of the OFD locking loop. Above 600 kHz offset frequency, the phase noise follows that of the free-running soliton, which is likely to be affected by the noise of the pump laser20. Phase noises at 1 and 10 kHz offset frequencies are extracted for all division ratios and are plotted in Fig. 3d. The phase noises follow the 1/N2 rule, validating the OFD.

The measured phase noise for the OFD soliton repetition rate is low for a microwave or mmWave oscillator. For comparison, phase noises of Keysight E8257D PSG signal generator (standard model) at 1 and 10 kHz are given in Fig. 3d after scaling the carrier frequency to 100 GHz. At 10 kHz offset frequency, our integrated OFD oscillator achieves a phase noise of −115 dBc Hz−1, which is 20 dB better than a standard PSG signal generator. When comparing to integrated microcomb oscillators that are stabilized to long optical fibres30, our integrated oscillator matches the phase noise at 10 kHz offset frequency and provides better phase noise below 5 kHz offset frequency (carrier frequency scaled to 100 GHz). We speculate this is because our photonic chip is rigid and small when compared to fibre references and thus is less affected by environmental noises such as vibration and shock. This showcases the capability and potential of integrated photonic oscillators. When comparing to integrated photonic microwave and mmWave oscillators, our oscillator shows exceptional performance: at 10 kHz offset frequency, its phase noise is more than two orders of magnitude better than other demonstrations, including the free-running SiN soliton microcomb oscillators21,26 and the very recent single-laser OFD40. A notable exception is the recent work of Kudelin et al.41, in which 6 dB better phase noise was achieved by stabilizing a 20 GHz soliton microcomb oscillator to a microfabricated Fabry–Pérot reference cavity.

The OFD soliton microcomb is then sent to a high-power, high-speed flip-chip bonded CC-MUTC PD for mmWave generation. Similar to a uni-travelling carrier PD42, the carrier transport in the CC-MUTC PD depends primarily on fast electrons that provide high speed and reduce saturation effects due to space-charge screening. Power handling is further enhanced by flip-chip bonding the PD to a gold-plated coplanar waveguide on an aluminium nitride submount for heat sinking43. The PD used in this work is an 8-μm-diameter CC-MUTC PD with 0.23 A/W responsivity at 1,550 nm wavelength and a 3 dB bandwidth of 86 GHz. Details of the CC-MUTC PD are described elsewhere44. Whereas the power characterization of the generated mmWave is straightforward, phase noise measurement at 100 GHz is not trivial as the frequency exceeds the bandwidth of most phase noise analysers. One approach is to build two identical yet independent oscillators and down-mix the frequency for phase noise measurement. However, this is not feasible for us due to the limitation of laboratory resources. Instead, a new mmWave to microwave frequency division method is developed to coherently divide down the 100 GHz mmWave to 20 GHz microwave, which can then be directly measured on a phase noise analyser (Fig. 4a).

Fig. 4: Electrical domain characterization of mmWaves generated from integrated OFD.
figure 4

a, Simplified schematic of frequency division. The 100 GHz mmWave generated by integrated OFD is further divided down to 20 GHz for phase noise characterization. b, Typical electrical spectra of the VCO after mmWave to microwave frequency division. The VCO is phase stabilized to the mmWave generated with the OFD soliton (red) or free-running soliton (black). To compare the two spectra, the peaks of the two traces are aligned in the figure. RBW, resolution bandwidth. c, Phase noise measurement in the electrical domain. Phase noise of the VCO after mmFD is directly measured by the phase noise analyser (dashed green). Scaling this trace to a carrier frequency of 100 GHz yields the phase noise upper bound of the 100 GHz mmWave (red). For comparison, phase noises of reference lasers (orange) and the OFD soliton repetition rate (blue) measured in the optical domain are shown. d, Measured mmWave power versus PD photocurrent at −2 V bias. A maximum mmWave power of 9 dBm is recorded. e, Measured mmWave phase noise at 1 and 10 kHz offset frequencies versus PD photocurrent.

In this mmFD, the generated 100 GHz mmWave and a 19.7 GHz VCO signal are sent to a harmonic radio-frequency (RF) mixer (Pacific mmWave, model number WM/MD4A), which creates higher harmonics of the VCO frequency to mix with the mmWave. The mixer outputs the frequency difference between the mmWave and the fifth harmonics of the VCO frequency: Δf = fr − 5fVCO2 and Δf is set to be around 1.16 GHz. Δf is then phase locked to a stable local oscillator (fLO2) by feedback control of the VCO frequency. This stabilizes the frequency and phase of the VCO to that of the mmWave within the servo locking bandwidth, as fVCO2 = (fr − fLO2)/5. The electrical spectrum and phase noise of the VCO are then measured directly on the phase noise analyser and are presented in Fig. 4b,c. The bandwidth of the mmFD servo loop is 150 kHz. The phase noise of the 19.7 GHz VCO can be scaled back to 100 GHz to represent the upper bound of the mmWave phase noise. For comparison, the phase noise of reference lasers and the OFD soliton repetition rate measured in the optical domain with dual-tone delayed self-heterodyne interferometry method are also plotted. Between 100 Hz to 100 kHz offset frequency, the phase noise of soliton repetition rate and the generated mmWave match very well with each other. This validates the mmFD method and indicates that the phase stability of the soliton repetition rate is well transferred to the mmWave. Below 100 Hz offset frequency, measurements in the optical domain suffer from phase drift in the 200 m optical fibre in the interferometry and thus yield phase noise higher than that measured with the electrical method.

Finally, the mmWave phase noise and power are measured versus the MUTC PD photocurrent from 1 to 18.3 mA at −2 V bias by varying the illuminating optical power on the PD. Although the mmWave power increases with the photocurrent (Fig. 4d), the phase noise of the mmWave remains almost the same for all different photocurrents (Fig. 4e). This suggests that low phase noise and high power are simultaneously achieved. The achieved power of 9 dBm is one of the highest powers ever reported at 100 GHz frequency for photonic oscillators36.

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SWIFT-LOCK next generation DSLR bottom camera strap mount

SWIFT-LOCK next generation DSLR or mirrorless camera strap

Photography enthusiasts and professionals alike are always on the lookout for ways to enhance their photographs and make carrying cameras easier. The SPINN SWIFT-LOCK system is a new camera carrying solution that promises to do just that. It’s designed for photographers who are constantly moving, whether they’re climbing mountains or weaving through city streets. This system is set to change the way photographers carry and use their DSLR or mirrorless cameras.

At the heart of the SPINN SWIFT-LOCK system is a magnetic quick-release mechanism. This feature allows for fast camera swaps or detachment from the strap with little effort. The quick-release plate attaches quickly to the camera strap mount, so photographers can be ready at a moment’s notice to capture those once-in-a-lifetime shots.

Early bird benefits are now available for the inventive project from roughly $60 or £51 (depending on current exchange rates), offering a considerable discount of approximately 25% off the purchase price, while the Kickstarter crowd funding is under way. But the SPINN SWIFT-LOCK system isn’t just about speed. It also focuses on stability and comfort. The design keeps the camera secure and close to the body, reducing the risk of it swinging or bouncing when you’re on the move. This means that no matter where you are—pushing through a crowd or hiking on a rough trail—your camera is safe.

Bottom camera strap mount

One of the key benefits of the SPINN SWIFT-LOCK is its universal compatibility. It works smoothly with many tripod and accessory brands, including Arca and Peak Design. This means photographers can easily integrate it with the equipment they already own.

SWIFT-LOCK camera strap mount

The camera strap mount itself is designed with both functionality and sustainability in mind. Made from recycled materials, it’s adjustable, lightweight, and compact, without sacrificing strength. The strap’s material slides smoothly, and it has carbon fiber-reinforced quick-adjusters, ensuring a comfortable and durable carrying experience.

If the SWIFT-LOCK campaign successfully raises its required pledge goal and the project completion progresses smoothly, worldwide shipping is expected to take place sometime around June 2024. To learn more about the SWIFT-LOCK DSLR or mirrorless camera strap mount project audit the promotional video below.

Produced in Germany, the SPINN SWIFT-LOCK system is a testament to precision and quality. It’s perfect for photographers who lead an active lifestyle and often find themselves outdoors, engaging in activities like climbing or cycling.

Overall, the SPINN SWIFT-LOCK is more than just a camera strap mount. It’s a comprehensive carrying solution that meets the needs of photographers who prioritize quick access to their camera, comfort while on the move, and compatibility with their existing gear. The quick-release mechanism, stabilization features, and wide-ranging compatibility make the SPINN SWIFT-LOCK an essential tool for any photographer’s collection.

For a complete list of all available pledges, stretch goals, extra media and material specifications for the DSLR or mirrorless camera strap mount, jump over to the official SWIFT-LOCK crowd funding campaign page by following the link below.

Source : Kickstarter

Disclaimer: Participating in Kickstarter campaigns involves inherent risks. While many projects successfully meet their goals, others may fail to deliver due to numerous challenges. Always conduct thorough research and exercise caution when pledging your hard-earned money.

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NVIDIA RTX 500 and 1000 Pro Ada Generation laptop GPUs

NVIDIA RTX Ada Generation Laptop GPUs

NVIDIA has unveiled its latest advancements in graphics processing technology with the introduction of the RTX 500 and 1000 Ada Generation GPUs for laptops. This development is set to make waves among professionals who rely on high-powered computing for tasks such as AI generation, graphic design, and video editing. These new GPUs are tailored to boost productivity and performance, especially in the increasingly common hybrid work environments.

The new RTX 500 and 1000 GPUs are built on NVIDIA’s Ada Lovelace architecture, which is designed to meet the high demands of industry professionals. This architecture includes an NPU and Tensor Cores, which are essential for AI acceleration. This feature is particularly important for handling complex AI tasks more efficiently.

Professionals working with generative AI will find the RTX 500 GPU to be a significant upgrade, offering a performance increase of up to 14 times, which greatly speeds up the creative process and delivers faster results. For those involved in photo editing, the GPU’s capabilities can triple the speed of AI-powered enhancements, making it a valuable tool for improving workflow. Additionally, 3D rendering tasks can be completed up to ten times faster compared to traditional CPU-only setups, which is a major leap forward for professionals in this field.

NVIDIA RTX Ada Generation Laptop GPUs

The RTX 500 and 1000 GPUs are not just about raw performance; they are also designed with specific professional applications in mind. They are particularly effective for high-quality video conferencing and streaming, which is beneficial for video editors and graphic designers. These GPUs are also capable of handling advanced rendering, data science, and deep learning tasks with ease.

NVIDIA’s progress is further emphasized by the inclusion of third-generation RT Cores, fourth-generation Tensor Cores, and Ada Generation CUDA cores in these GPUs. These components work together to improve ray tracing, deep learning, and overall graphics performance. Additional features such as dedicated GPU memory, DLSS 3 for enhanced image quality, and an AV1 encoder for more efficient video compression are crucial for tasks like streaming and video conferencing.

Recognizing the need for portability in today’s professional world, NVIDIA has made sure that these new GPUs are integrated into sleek, lightweight laptops. This ensures that professionals can enjoy top-tier performance without being tied down to a desk, making these laptops perfect for those who are always on the move.

Revolutionizing Mobile Graphics with NVIDIA’s RTX 500 and 1000 GPUs

The RTX 500 and 1000 GPUs are set to be available in the spring, with offerings from leading manufacturers such as Dell Technologies, HP, Lenovo, and MSI. NVIDIA’s latest launch is poised to meet the needs of tech-savvy professionals who are looking to enhance their work efficiency and creative capabilities. With these new GPUs, NVIDIA continues to push the boundaries of what’s possible in mobile computing, providing powerful tools for a wide range of professional applications.

NVIDIA’s introduction of the RTX 500 and 1000 Ada Generation GPUs for laptops marks a significant leap in graphics processing technology. These GPUs are engineered to cater to the needs of professionals who demand high-powered computing for a variety of tasks, including AI generation, graphic design, and video editing. The integration of these GPUs into laptops is poised to enhance productivity and performance significantly, particularly for those who operate in flexible work settings that blend in-office and remote work.

The Ada Lovelace architecture at the core of the RTX 500 and 1000 GPUs is a cutting-edge design that addresses the intensive requirements of industry professionals. It incorporates an NPU (Neural Processing Unit) and Tensor Cores, which are pivotal for AI acceleration. These components are specifically designed to manage complex AI-driven tasks with greater efficiency, offering a substantial improvement over previous generations.

Unleashing Creativity with Enhanced AI and Rendering Capabilities

For those engaged in generative AI projects, the RTX 500 GPU represents a significant advancement, delivering a performance boost that can reach up to 14 times that of earlier models. This enhancement dramatically accelerates the creative process and yields quicker outcomes. In the realm of photo editing, the GPU’s prowess in AI-powered enhancements can triple processing speeds, streamlining workflows for professionals. Furthermore, 3D rendering tasks can be executed up to ten times faster than with conventional CPU-based systems, representing a substantial stride in efficiency for those specializing in 3D graphics.

The RTX 500 and 1000 GPUs are engineered not only for sheer performance but also for specific professional use cases. They excel in applications that require high-quality video conferencing and streaming, which is advantageous for video editors and graphic designers. These GPUs are adept at managing complex tasks such as advanced rendering, data science, and deep learning, making them versatile tools for a wide array of professional applications.

Advanced Cores and Features for Optimal Performance

NVIDIA’s advancements are underscored by the inclusion of third-generation RT Cores, fourth-generation Tensor Cores, and Ada Generation CUDA cores in the new GPUs. These components synergize to elevate ray tracing, deep learning, and overall graphics performance to new heights. Additional features like dedicated GPU memory, DLSS 3 for enhanced image quality, and an AV1 encoder for more efficient video compression are indispensable for professionals engaged in streaming and video conferencing.

In recognition of the growing demand for mobility in the professional sphere, NVIDIA has ensured that these powerful GPUs are incorporated into sleek, lightweight laptops. This integration allows professionals to access top-tier performance on the go, liberating them from the confines of a stationary workspace and catering to the needs of those with dynamic lifestyles.

The RTX 500 and 1000 GPUs are slated for release in the spring, with a lineup from prominent manufacturers such as Dell Technologies, HP, Lenovo, and MSI. NVIDIA’s latest offerings are set to fulfill the aspirations of tech-savvy professionals seeking to amplify their work efficiency and creative potential. With these new GPUs, NVIDIA continues to redefine the capabilities of mobile computing, equipping a diverse range of professionals with robust tools for their trade.

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Improving your Midjourney prompts for amazing AI art generation

Improving your Midjourney prompts for amazing AI art

In the ever-evolving world of digital art, artists and designers are constantly seeking new ways to push the boundaries of creativity. Midjourney AI stands at the forefront of this exploration, offering a powerful tool that blends artificial intelligence with artistic expression. By understanding and manipulating the various parameters of Midjourney AI, creatives can unlock a new realm of possibilities, crafting artwork that resonates with their unique vision. This guide offers further insight into writing Midjourney prompts to take your AI artwork to the next level.

To begin with, the “stylize” value is a critical setting that influences the balance between an abstract interpretation and a more literal representation of your ideas. If you’re aiming for a piece that exudes a strong artistic character, increasing the stylize value can infuse your work with an abstract touch. Conversely, a lower value keeps the AI’s output more faithful to your specific instructions, ensuring that the details you envision are accurately reflected in the final piece.

crafting Midjourney Prompts for AI art

Moving on, the “style raw” setting is another lever at your disposal. This feature allows you to steer the AI’s default output towards the artistic outcome you have in mind. By adjusting this setting, you can refine the AI’s interpretation of your prompt, aligning it more closely with your expectations.

A simple yet effective technique to guide the AI is to provide a rough sketch. Even a basic outline created in a program like Photoshop or Canva can significantly influence the AI’s creative trajectory. Your sketch acts as a navigational tool, directing the AI towards the artistic destination you’re aiming for.

Midjourney prompts writing take a step to a new level, when you upload a reference image to Discord and incorporate it into your prompt, you give the AI a clear visual target to aim for. This method can greatly improve the specificity and relevance of your artwork, ensuring that the final product aligns with your vision.

Writing Midjourney Prompts

Watch the video below kindly created by Future Tech Pilot on creating the best possible prompts to get fantastic results from Midjourney.

Here are some other articles you may find of interest on the subject of Midjourney Styles :

The “image weight” parameter offers you the ability to dictate how much your reference image should influence the AI’s output. A higher image weight means the AI will adhere more to your provided image, while a lower weight allows for more creative freedom and interpretation by the AI.

Keywords play a subtle yet powerful role in shaping the AI’s output. Including terms like “unsplash” can nudge the AI towards certain styles, such as cinematic or photorealistic, helping to capture the ambiance you desire for your artwork.

For those looking to maintain thematic consistency across multiple pieces, the “describe” feature is invaluable. It enables you to generate descriptive prompts from an existing image, ensuring that each piece in a series shares a common thread.

Another technique to influence the AI’s artistic direction is to use a style reference (D-SREF) with an image. This approach allows you to guide the AI towards a specific style without the need for explicit descriptions, fostering a more organic interpretation by the AI.

Finally, the “style weight” (D-SSW) adjustment is a fine-tuning tool that lets you control the extent to which your chosen style affects the final artwork. This is essential for achieving the precise level of stylistic influence you’re after.

Midjourney AI presents a rich toolkit for artists and designers to refine their AI art prompts. Through experimentation with settings like “stylize,” “style raw,” and various weight adjustments, you can exert considerable control over the artistic output. By using hints, the “describe” feature, and manipulating style references, you can further customize the creative process to your liking.

Exploring the Depths of Digital Art with Midjourney AI

The key to producing art that truly captures your vision is a willingness to explore and experiment with these tools. Each adjustment you make is a step toward mastering the art of AI-generated imagery, enabling you to create with both accuracy and creativity. As you continue to experiment with Midjourney AI, you’ll find that the power to shape your artistic creations is at your fingertips, ready to be harnessed and directed in ways that were once unimaginable.

The realm of digital art is a dynamic and ever-changing landscape where artists and designers leverage technology to express their creativity. Midjourney AI emerges as a cutting-edge tool that marries the capabilities of artificial intelligence with the nuances of human artistic expression. By mastering the various parameters within Midjourney AI, creatives can unlock a vast array of possibilities, allowing them to produce artwork that truly reflects their individual vision.

The “stylize” value is a pivotal setting within Midjourney AI that determines the balance between abstract and literal interpretations of a concept. For artwork that radiates a distinct artistic flair, increasing the stylize value can introduce an abstract quality to the piece. On the other hand, a lower value will result in the AI producing an output that is more aligned with the specific details of your vision, ensuring that the nuances you imagine are precisely captured in the final artwork.

Enhancing Artistic Direction with Midjourney AI’s Advanced Features

The “style raw” setting is another powerful tool at an artist’s disposal. This parameter allows you to influence the AI’s default creative process, guiding it towards the artistic outcome you envision. By fine-tuning this setting, you can adjust the AI’s interpretation of your instructions, ensuring that the output is more closely aligned with your expectations.

A straightforward yet impactful method to direct the AI is through the use of a rough sketch. Even a simple outline crafted in software like Photoshop or Canva can significantly shape the AI’s creative path. Your sketch serves as a compass, pointing the AI towards the artistic destination you seek.

Incorporating an image prompt by uploading a reference picture to Discord and using it in your prompt provides the AI with a clear visual benchmark. This technique can enhance the specificity and relevance of your artwork, guaranteeing that the end result is in harmony with your original concept.

The “image weight” parameter allows you to control how much your reference image influences the AI’s output. A higher image weight compels the AI to closely follow your provided image, whereas a lower weight permits the AI more creative leeway and interpretation.

Keywords have a subtle yet potent effect on the AI’s creative output. Including terms like “unsplash” can guide the AI towards specific styles, such as cinematic or photorealistic, aiding in capturing the atmosphere you wish to convey in your artwork.

For artists aiming to achieve a consistent theme across a series of works, the “describe” feature is incredibly useful. It enables the generation of descriptive prompts from an existing image, ensuring a cohesive aesthetic thread throughout the series.

Mastering AI Art with Midjourney AI’s Customizable Parameters

Utilizing a style reference (D-SREF) with an image is another strategy to steer the AI towards a particular artistic style without explicit descriptions. This method encourages a more natural interpretation by the AI, allowing for a unique artistic expression.

Finally, the “style weight” (D-SSW) adjustment is a precision tool that lets you dictate the degree to which your selected style influences the final piece. This fine-tuning is crucial for attaining the exact stylistic impact you desire.

Midjourney AI offers a comprehensive suite of tools for artists and designers to refine their AI art prompts. By experimenting with settings such as “stylize,” “style raw,” and various weight adjustments, you can exert significant influence over the artistic output. Employing strategies like the “describe” feature and manipulating style references allows for further customization of the creative process.

The secret to creating art that truly embodies your vision lies in the willingness to explore and experiment with these tools. Each modification is a step towards perfecting the craft of AI-generated imagery, empowering you to create with both precision and inventiveness. As you delve deeper into the capabilities of Midjourney AI, you’ll discover that the ability to shape your artistic creations is within reach, offering unprecedented opportunities to push the boundaries of digital art.

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NVIDIA NVIDIA RTX 2000 Ada Generation GPU

NVIDIA NVIDIA RTX 2000 Ada Generation GPU

NVIDIA has just unveiled a new graphics card that’s set to transform the way professionals work across various industries. The RTX 2000 Ada Generation GPU is not just an upgrade; it’s a leap forward, offering up to 50% more performance than its predecessor, the RTX A2000 12 GB. This new card is designed to fit into compact workstations, yet it packs a punch with 16 GB of memory, making it more than capable of handling complex tasks and managing high-resolution content with ease. The NVIDIA RTX 2000 Ada features the latest technologies in the NVIDIA Ada Lovelace GPU architecture, including:

  • Third-generation RT Cores: Up to 1.7x faster ray tracing performance for high-fidelity, photorealistic rendering.
  • Fourth-generation Tensor Cores: Up to 1.8x AI throughput over the previous generation, with structured sparsity and FP8 precision to enable higher inference performance for AI-accelerated tools and applications.
  • CUDA cores: Up to 1.5x the FP32 throughput of the previous generation for significant performance improvements in graphics and compute workloads.
  • Power efficiency: Up to a 2x performance boost across professional graphics, rendering, AI and compute workloads, all within the same 70 W of power as the previous generation.
  • Immersive workflows: Up to 3x performance for virtual-reality workflows over the previous generation.
  • 16 GB of GPU memory: An expanded canvas enables users to tackle larger projects, along with support for error correction code memory to deliver greater computing accuracy and reliability for mission-critical applications.
  • DLSS 3: Delivers a breakthrough in AI-powered graphics, significantly boosting performance by generating additional high-quality frames.
  • AV1 encoder: Eighth-generation NVIDIA Encoder, aka NVENC, with AV1 support is 40% more efficient than H.264, enabling new possibilities for broadcasters, streamers and video callers.

Professionals from all walks of life, including architects, engineers, and content creators, stand to benefit from the RTX 2000 Ada’s enhanced capabilities. For those in architecture and urban planning, the GPU accelerates visualization and structural analysis, making it easier to bring projects to life. Product designers and engineers will appreciate the ability to iterate designs more quickly, streamlining the development process. Content creators, on the other hand, will enjoy smooth editing experiences, even when dealing with high-resolution videos and images. The GPU also supports real-time data processing, which is essential for industries that rely on AI-driven intelligence, such as medical devices, manufacturing, and retail.

NVIDIA NVIDIA RTX 2000

In the realm of virtual reality, the RTX 2000 Ada stands out with its robust support for immersive graphics. It utilizes NVIDIA’s DLSS and ray-tracing technologies to create incredibly realistic images, enhancing enterprise workflows in VR and providing users with an experience that’s closer to reality than ever before.

The technological innovations in the RTX 2000 Ada are impressive. It features third-generation RT Cores for quicker ray tracing, fourth-generation Tensor Cores for enhanced AI throughput, and improved CUDA cores for handling both graphics and compute workloads efficiently. Despite these advancements, the GPU remains power-efficient, delivering twice the performance within a 70 W power envelope. It also includes DLSS 3 technology for AI-powered graphics enhancement and an AV1 encoder, which optimizes video streaming and calling.

NVIDIA NVIDIA RTX 2000 features

 

The feedback from early adopters of the RTX 2000 Ada has been overwhelmingly positive. Companies like Dassault Systèmes, Rob Wolkers Design and Engineering, and WSP have already experienced the GPU’s exceptional performance, versatility, and large memory capacity. These attributes are essential for managing the complex tasks that professionals encounter daily.

NVIDIA ensures that users of the RTX 2000 Ada have the support they need with the latest RTX Enterprise Driver. This driver introduces new features such as Video TrueHDR and Video Super Resolution, enhancing the overall user experience. The GPU is available globally through distribution partners and will be included in systems from major manufacturers like Dell Technologies, HP, and Lenovo starting in April.

Specifications

NVIDIA NVIDIA RTX 2000 specifications

The release of the NVIDIA RTX 2000 Ada Generation GPU is a significant event for professionals who rely on powerful graphics processing. It’s a versatile tool that caters to a wide range of industries and workflows, reflecting NVIDIA’s commitment to pushing the boundaries of graphics technology. For more information on full specifications jump over to the official NVIDIA product page.

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AI video generation tools – Google LUMIERE, Runway and more

AI video generation tools Runway LUMIERE Alchemy RenderNet

The world of content creation is rapidly changing, and artificial intelligence (AI) is at the heart of this transformation. Although still in their early stages of development and only capable of creating short animations and clips. These tools are not just for experts; they are becoming more user-friendly, allowing people with various skill levels to produce professional-looking content. Video generation tools powered by AI are reshaping how we create and consume media, offering new levels of control and creativity.

Google has this month unveiled its new AI video generator in the form of LUMIERE. Google has also stepped into the arena with Lumiere, a tool that boasts a range of features including the ability to convert text to video and generate different styles. This simplifies the video creation process significantly, making it easier for anyone to produce high-quality content without the need for extensive training or experience. Lumiere by Google is setting a new standard for ease of use in video production.

Another notable advancements is the motion brush by Runway, which gives creators the ability to animate scenes with a level of precision that was once out of reach for many. This tool is making complex animations more accessible, opening up a world of possibilities for creators. Runway’s motion brush is a game-changer in the industry.

Runway Motion Brush

Learn to generate expressions using Multi Motion Brush in Gen-2. Create a character using Gen-2. Use Multi Motion Brush to draw various masks on the character’s face including eyebrows, eyes, and mouth positions work best. Experiment with different directions for each brush, taking into account the expression you are going for in the output

The realm of 3D graphics is also benefiting from AI, with new tools that can turn 3D assets into stunning, high-resolution visuals. RenderNet, for example, is improving the way character imagery is produced, ensuring that scenes remain consistent, which is crucial for maintaining the integrity of a project.

AI video generation tools overview

AI is not just transforming the way we create content; it’s also changing how we interact. The integration of Insight face swap into platforms like Discord is opening up new avenues for real-time interaction, making online communication more engaging and personal. Insight face swap is revolutionizing the way we communicate in virtual spaces.

Google LUMIERE AI video creator

However, the rise of AI in video generation is not without its challenges. The technology’s potential for spreading misinformation and the complexities of language translation are areas that require careful consideration and responsible management. Despite these challenges, the advancements in AI are undeniable. Creating animatable avatars and 3D depth maps from simple images is now more straightforward, adding layers of depth and engagement to projects. Leonardo’s Alchemy versions are even offering free access to AI tools for a limited time, making these powerful capabilities available to a broader audience.

In the field of graphic design, AI is speeding up the design process with advancements in color customization and vectorization, allowing for greater personalization and efficiency. The traditional stock photography industry is feeling the impact as well, with AI-generated images beginning to offer an alternative source for visual content.

Education in AI is also gaining momentum, with organizations like 11 Labs leading the charge in teaching the next generation about these technologies. Real-time avatars are setting the stage for a future of creative interactivity, providing immersive experiences that were once the stuff of science fiction.

AI-generated films are showcasing the potential of these tools to foster new forms of storytelling, pushing the boundaries of our imagination. As these tools continue to evolve and become more accessible, they hold the promise of a new era in content creation and consumption. AI-generated films are a testament to the innovative power of these technologies.

The influence of AI video generation tools is only just starting to be felt, and the future is poised to bring even more remarkable developments in this exciting field. The creative industry is on the cusp of a major shift, and AI is the driving force behind it. As we look ahead, it’s clear that the ways we create and interact with media will continue to evolve, offering endless opportunities for innovation and expression.

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AVerMedia next generation PCIe live streaming capture cards

AVerMedia next generation PCIe live streaming capture cards

In the dynamic world of live streaming and content creation, AVerMedia Technologies has stepped up its game by unveiling two new PCIe capture cards that promise to take your streaming experience to the next level. The HDMI 2.1 Live Gamer 4K 2.1 (GC575) and the Live Streamer ULTRA HD (GC571) are the latest additions to AVerMedia’s lineup, designed to cater to the diverse needs of content creators, from the tech-savvy to those just starting out.

Live streaming capture cards

  • Live Gamer 4K 2.1: Available now at a suggested retail price of US $269.99.
  • Live Streamer ULTRA HD: Available now at a suggested retail price of $179.99.

2.1 Live Gamer 4K 2.1 (GC575)

The Live Gamer 4K 2.1, with a price tag of $269.99, is a top-of-the-line option for those looking to produce the highest quality content. It boasts a 4K144 pass-through with HDR and VRR, ensuring that your gameplay is displayed in the most vivid and fluid manner possible. Additionally, it captures video at 60 frames per second in 4K, making it an ideal choice for creators who aim to deliver ultra-high-definition content to their audience.

Specifications

  • Interface: PCIe Gen 3 x4
  • Video Input: HDMI 2.1
  • Video Output (Pass-Through): HDMI 2.1
  • Max Pass-Through Resolutions: 2160p144 HDR/VRR, 3440x1440p 120 HDR/VRR, 1440p240 HDR/VRR, 1080p360 HDR/VRR
  • Max Capture Resolution: 2160p60
  • Video Format: YUY2, NV12, RGB24, P010(HDR)
  • Dimensions: (W x D x H): 121 x 160.5 x 21.5 mm (4.76 x 6.32 x 0.85 in)
  • Weight: 150.5 g (5.31 oz)
  • System Requirements
  • Windows 10 x64 / 11 x64 or later
  • Desktop: Intel Core i5-6XXX / AMD Ryzen 3 XXX or above + NVIDIA GTX 1060 / AMD RX 5700 or above
  • 8 GB RAM recommended (Dual-channel)
  • Make sure that both your display and console (PS5, PS4 Pro, Xbox Series X/S, Xbox One X) support HDMI 2.1 connections.
  • If your monitor supports built-in DSC (Display Stream Compression), be aware that the maximum supported video pass-through might be 4K120.

Live Streamer ULTRA HD (GC571)

For those who are mindful of their budget but still want to produce high-quality streams, the Live Streamer ULTRA HD is an attractive alternative. Priced at $179.99, it offers 4K streaming and capturing capabilities, providing your viewers with crisp, high-resolution visuals. Its ease of use and compact size make it a great fit for newcomers to streaming or those with space constraints.

Both capture cards are designed to integrate smoothly with AVerMedia’s RECentral software, which simplifies the process of streaming to multiple platforms simultaneously. This means you can share your content with a wider audience across various channels with minimal hassle. The software’s intuitive interface also helps you focus more on creating engaging content rather than getting bogged down by technical details.

AVerMedia’s commitment to enhancing the streaming experience is evident in these new offerings. The Live Gamer 4K 2.1 and the Live Streamer ULTRA HD are packed with features that address the needs of streamers at different levels of expertise and financial considerations. With the ability to capture 4K60 video and support for 4K144 pass-through with HDR/VRR, these capture cards are set to improve the quality of your live streams significantly. Whether you’re a seasoned content creator or just starting, AVerMedia provides the tools you need to produce top-notch content and grow your audience.

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