Q.ANT, the leading startup for photonic computing, today announced the launch of its first commercial product - a photonics-based Native Processing Unit (NPU) built on the company's compute architecture LENA - Light Empowered Native Arithmetics. The product is fully compatible with today's existing computing ecosystem as it comes on the industry-standard PCI-Express. The Q.ANT NPU executes complex, non-linear mathematics natively using light instead of electrons, promising to deliver at least 30 times greater energy efficiency and significant computational speed improvements over traditional CMOS technology. Designed for compute-intensive applications such as AI Inference, machine learning, and physics simulation, the Q.ANT NPU has been proven to solve real-world challenges, including number recognition for deep neural network inference (see the recent press release regarding Cloud Access to NPU).

"With our photonic chip technology now available on the standard PCIe interface, we're bringing the incredible power of photonics directly into real-world applications. For us, this is not just a processor—it's a statement of intent: Sustainability and performance can go hand in hand," said Dr. Michael Förtsch, CEO of Q.ANT. "For the first time, developers can create AI applications and explore the capabilities of photonic computing, particularly for complex, nonlinear calculations. For example, experts calculated that one GPT-4 query today uses 10 times more electricity than a regular internet search request. Our photonic computing chips offer the potential to reduce the energy consumption for that query by a factor of 30."

Mobilint, an edge AI chip company led by CEO Dongjoo Shin, is set to make waves at the upcoming AI Hardware & Edge AI Summit 2024 in Silicon Valley. The three-day event, starting on September 10th, will showcase Mobilint's latest innovations in AI chip technology. The company will demonstrate live demos of its high-efficiency SoC 'REGULUS' for on-device AI and high-performance acceleration chip 'ARIES' for on-premises AI.

The AI Hardware Summit is an annual event where global IT giants such as Microsoft, NVIDIA, Google, Meta, and AMD, along with prominent startups, gather to share their developments in AI and machine learning. This year's summit features world-renowned AI experts as speakers, including Andrew Ng, CEO of Landing AI, and Mark Russinovich, CTO of Microsoft Azure.

While we are all used to having a system with a CPU, GPU, and, recently, NPU—X-Silicon Inc. (XSi), a startup founded by former Silicon Valley veterans—has unveiled an interesting RISC-V processor that can simultaneously handle CPU, GPU, and NPU workloads in a chip. This innovative chip architecture, which will be open-source, aims to provide a flexible and efficient solution for a wide range of applications, including artificial intelligence, virtual reality, automotive systems, and IoT devices. The new microprocessor combines a RISC-V CPU core with vector capabilities and GPU acceleration into a single chip, creating a versatile all-in-one processor. By integrating the functionality of a CPU and GPU into a single core, X-Silicon's design offers several advantages over traditional architectures. The chip utilizes the open-source RISC-V instruction set architecture (ISA) for both CPU and GPU operations, running a single instruction stream. This approach promises lower memory footprint execution and improved efficiency, as there is no need to copy data between separate CPU and GPU memory spaces.

Called the C-GPU architecture, X-Silicon uses RISC-V Vector Core, which has 16 32-bit FPUs and a Scaler ALU for processing regular integers as well as floating point instructions. A unified instruction decoder feeds the cores, which are connected to a thread scheduler, texture unit, rasterizer, clipping engine, neural engine, and pixel processors. All is fed into a frame buffer, which feeds the video engine for video output. The setup of the cores allows the users to program each core individually for HPC, AI, video, or graphics workloads. Without software, there is no usable chip, which prompts X-Silicon to work on OpenGL ES, Vulkan, Mesa, and OpenCL APIs. Additionally, the company plans to release a hardware abstraction layer (HAL) for direct chip programming. According to Jon Peddie Research (JPR), the industry has been seeking an open-standard GPU that is flexible and scalable enough to support various markets. X-Silicon's CPU/GPU hybrid chip aims to address this need by providing manufacturers with a single, open-chip design that can handle any desired workload. The XSi gave no timeline, but it has plans to distribute the IP to OEMs and hyperscalers, so the first silicon is still away.

George Hotz and his startup operation (Tiny Corporation) appeared ready to completely abandon AMD Radeon GPUs last week, after experiencing a period of firmware-related headaches. The original plan involved the development of a pre-orderable $15,000 TinyBox AI compute cluster that housed six XFX Speedster MERC310 RX 7900 XTX graphics cards, but software/driver issues prompted experimentation via alternative hardware routes. A lot of media coverage has focused on the unusual adoption of consumer-grade GPUs—Tiny Corp.'s struggles with RDNA 3 (rather than CDNA 3) were maneuvered further into public view, after top AMD brass pitched in.

The startup's social media feed is very transparent about showcasing everyday tasks, problem-solving and important decision-making. Several Acer Predator BiFrost Arc A770 OC cards were purchased and promptly integrated into a colorfully-lit TinyBox prototype, but Hotz & Co. swiftly moved onto Team Green pastures. Tiny Corp. has begrudgingly adopted NVIDIA GeForce RTX 4090 GPUs. Earlier today, it was announced that work on the AMD-based system has resumed—although customers were forewarned about anticipated teething problems. The surprising message arrived in the early hours: "a hard to find 'umr' repo has turned around the feasibility of the AMD TinyBox. It will be a journey, but it gives us an ability to debug. We're going to sell both, red for $15,000 and green for $25,000. When you realize your pre-order you'll choose your color. Website has been updated. If you like to tinker and feel pain, buy red. The driver still crashes the GPU and hangs sometimes, but we can work together to improve it."

George Hotz and his Tiny Corporation colleagues were pinning their hopes on AMD delivering some good news earlier this month. The development of a "TinyBox" AI compute cluster project hit some major roadblocks a couple of weeks ago—at the time, Radeon RX 7900 XTX GPU firmware was not gelling with Tiny Corp.'s setup. Hotz expressed "70% confidence" in AMD approving open-sourcing certain bits of firmware. At the time of writing this has not transpired—this week the Tiny Corp. social media account has, once again, switched to an "all guns blazing" mode. Hotz and Co. have publicly disclosed that they were dabbling with Intel Arc graphics cards, as of a few weeks ago. NVIDIA hardware is another possible route, according to freshly posted open thoughts.

Yesterday, it was confirmed that the young startup organization had paused its utilization of XFX Speedster MERC310 RX 7900 XTX graphics cards: "the driver is still very unstable, and when it crashes or hangs we have no way of debugging it. We have no way of dumping the state of a GPU. Apparently it isn't just the MES causing these issues, it's also the Command Processor (CP). After seeing how open Tenstorrent is, it's hard to deal with this. With Tenstorrent, I feel confident that if there's an issue, I can debug and fix it. With AMD, I don't." The $15,000 TinyBox system relies on "cheaper" gaming-oriented GPUs, rather than traditional enterprise solutions—this oddball approach has attracted a number of customers, but the latest announcements likely signal another delay. Yesterday's tweet continued to state: "we are exploring Intel, working on adding Level Zero support to tinygrad. We also added a $400 bounty for XMX support. We are also (sadly) exploring a 6x GeForce RTX 4090 GPU box. At least we know the software is good there. We will revisit AMD once we have an open and reproducible build process for the driver and firmware. We are willing to dive really deep into hardware to make it amazing. But without access, we can't."

Lately Tiny Corp. CEO—George Hotz—has used his company's social media account to publicly criticize AMD Radeon RX 7900 XTX GPU firmware. The creator of Tinybox, a pre-orderable $15,000 AI compute cluster, has not selected "traditional" hardware for his systems—it is possible that AMD's Instinct MI300X accelerator is quite difficult to acquire, especially for a young startup operation. The decision to utilize gaming-oriented XFX-branded RDNA 3.0 GPUs instead of purpose-built CDNA 3.0 platforms—for local model training and AI inference—is certainly a peculiar one. Hotz and his colleagues have encountered roadblocks in the development of their Tinybox system—recently, public attention was drawn to an "LLVM spilling bug." AMD President/CEO/Chair, Dr. Lisa Su, swiftly stepped in and promised a "good solution." Earlier in the week, Tiny Corp. reported satisfaction with a delivery of fixes—courtesy of Team Red's software engineering department. They also disclosed that they would be discussing matters with AMD directly, regarding the possibility of open-sourcing Radeon GPU MES firmware.

Subsequently, Hotz documented his interactions with Team Red representatives—he expressed 70% confidence in AMD approving open-sourcing certain bits of firmware in a week's time: "Call went pretty well. We are gating the commitment to 6x Radeon RX 7900 XTX on a public release of a roadmap to get the firmware open source. (and obviously the MLPerf training bug being fixed). We aren't open source purists, it doesn't matter to us if the HDCP stuff is open for example. But we need the scheduler and the memory hierarchy management to be open. This is what it takes to push the performance of neural networks. The Groq 500 T/s mixtral demo should be possible on a tinybox, but it requires god tier software and deep integration with the scheduler. We also advised that the build process for amdgpu-dkms should be more open. While the driver itself is open, we haven't found it easy to rebuild and install. Easy REPL cycle is a key driver for community open source. We want the firmware to be easy to rebuild and install also." Prior to this week's co-operations, Tiny Corp. hinted that it could move on from utilizing Radeon RX 7900 XTX, in favor of Intel Alchemist graphics hardware—if AMD's decision making does not favor them, Hotz & Co. could pivot to builds including Acer Predator BiFrost Arc A770 16 GB OC cards.

The TinyBox AI server system attracted plenty of media attention last week—its creator, George Hotz, decided to build with AMD RDNA 3.0 GPU hardware rather than the expected/traditional choice of CDNA 3.0. Tiny Corp. is a startup firm dealing in neural network frameworks—they currently "write and maintain tinygrad." Hotz & Co. are in the process of assembling rack-mounted 12U TinyBox systems for customers—an individual server houses an AMD EPYC 7532 processor and six XFX Speedster MERC310 Radeon RX 7900 XTX graphics cards. The Tiny Corp. social media account has engaged in numerous NVIDIA vs. AMD AI hardware debates/tirades—Hotz appears to favor the latter, as evidenced in his latest choice of components. ROCm support on Team Red AI Instinct accelerators is fairly mature at this point in time, but a much newer prospect on gaming-oriented graphics cards.

Tiny Corporation's unusual leveraging of Radeon RX 7900 XTX GPUs in a data center configuration has already hit a developmental roadblock. Yesterday, the company's social media account expressed driver-related frustrations in a public forum: "If AMD open sources their firmware, I'll fix their LLVM spilling bug and write a fuzzer for HSA. Otherwise, it's not worth putting tons of effort into fixing bugs on a platform you don't own." Hotz's latest complaint was taken onboard by AMD's top brass—Dr. Lisa Su responded with the following message: "Thanks for the collaboration and feedback. We are all in to get you a good solution. Team is on it." Her software engineers—within a few hours—managed to fling out a set of fixes in Tiny Corporation's direction. Hotz appreciated the quick turnaround, and proceeded to run a model without encountering major stability issues: "AMD sent me an updated set of firmware blobs to try. They are responsive, and there have been big strides in the driver in the last year. It will be good! This training run is almost 5 hours in, hasn't crashed yet." Tiny Corp. drummed up speculation about AMD open sourcing GPU MES firmware—Hotz disclosed that he will be talking (on the phone) to Team Red leadership.

Regional Chinese governments are attempting to prop up local AI startup companies with an intriguing "voucher" support system. A Financial Times article outlines "computing" support packages valued between "$140,000 to $280,000" for fledgling organizations involved in LLM training. Widespread shortages of AI chips and rising data center operation costs are cited as the main factors driving a rollout of strategic subsidizations. The big three—Alibaba, Tencent, and ByteDance—are reportedly less willing to rent out their AI-crunching servers, due to internal operations demanding lengthy compute sessions. China's largest technology companies are believed to hording the vast majority of NVIDIA AI hardware, while smaller competitors are believed to fighting over table scraps. US trade restrictions have further escalated supply issues, with lower-performance/China-specific models being rejected—AMD's Instinct MI309 AI accelerator being the latest example.

The "computer voucher" initiative could be the first part of a wider scheme—reports suggest that regional governing bodies (including Shanghai) are devising another subsidy tier for domestic AI chips. Charlie Chai, an 86Research analyst, reckons that the initial support package is only a short-term solution. He shared this observation with FT: "the voucher is helpful to address the cost barrier, but it will not help with the scarcity of the resources." The Chinese government is reportedly looking into the creation of an alternative state-run system, that will become less reliant on a "Big Tech" data center model. A proposed "East Data West Computing" project could produce a more energy-efficient cluster of AI data centers, combined with a centralized management system.

Tech giant Microsoft and Paris-based startup Mistral AI, an innovator in open-source AI model development, have announced a new multi-year partnership to accelerate AI innovation and expand access to Mistral's state-of-the-art models. The collaboration will leverage Azure's cutting-edge AI infrastructure to propel Mistral's research and bring its innovations to more customers globally. The partnership focuses on three core areas. First, Microsoft will provide Mistral with Azure AI supercomputing infrastructure to power advanced AI training and inference for Mistral's flagship models like Mistral-Large. Second, the companies will collaborate on AI research and development to push AI model's boundaries. And third, Azure's enterprise capabilities will give Mistral additional opportunities to promote, sell, and distribute their models to Microsoft customers worldwide.

However, an investment in a European startup can not go smoothly without the constant eyesight of the European Union authorities and regulators to oversee the deal. According to Bloomberg, an EU spokesperson on Tuesday claimed that the EU regulators will perform an analysis of Microsoft's investment into Mistral after receiving a copy of the agreement between the two parties. While there is no formal investigation yet, if EU regulators continue to probe Microsoft's deal and intentions, they could launch a complete formal investigation that could lead to the termination of Microsoft's plans. Of course, the formal investigation is still on hold, but investing in EU startups might become unfeasible for American tech giants if the EU regulators continue to push the scrutiny of every investment made in companies based on EU soil.

AI workloads are split into two different categories: training and inference. While training requires large computing and memory capacity, access speeds are not a significant contributor; inference is another story. With inference, the AI model must run extremely fast to serve the end-user with as many tokens (words) as possible, hence giving the user answers to their prompts faster. An AI chip startup, Groq, which was in stealth mode for a long time, has been making major moves in providing ultra-fast inference speeds using its Language Processing Unit (LPU) designed for large language models (LLMs) like GPT, Llama, and Mistral LLMs. The Groq LPU is a single-core unit based on the Tensor-Streaming Processor (TSP) architecture which achieves 750 TOPS at INT8 and 188 TeraFLOPS at FP16, with 320x320 fused dot product matrix multiplication, in addition to 5,120 Vector ALUs.

Having massive concurrency with 80 TB/s of bandwidth, the Groq LPU has 230 MB capacity of local SRAM. All of this is working together to provide Groq with a fantastic performance, making waves over the past few days on the internet. Serving the Mixtral 8x7B model at 480 tokens per second, the Groq LPU is providing one of the leading inference numbers in the industry. In models like Llama 2 70B with 4096 token context length, Groq can serve 300 tokens/s, while in smaller Llama 2 7B with 2048 tokens of context, Groq LPU can output 750 tokens/s. According to the LLMPerf Leaderboard, the Groq LPU is beating the GPU-based cloud providers at inferencing LLMs Llama in configurations of anywhere from 7 to 70 billion parameters. In token throughput (output) and time to first token (latency), Groq is leading the pack, achieving the highest throughput and second lowest latency.

Japanese tech billionaire and founder of the SoftBank Group, Masayoshi Son, is embarking on a hugely ambitious new project to build an AI chip company that aims to rival NVIDIA, the current leader in AI semiconductor solutions. Codenamed "Izanagi" after the Japanese god of creation, Son aims to raise up to $100 billion in funding for the new venture. With his company SoftBank having recently scaled back investments in startups, Son is now setting his sights on the red-hot AI chip sector. Izanagi would leverage SoftBank's existing chip design firm, Arm, to develop advanced semiconductors tailored for artificial intelligence computing. The startup would use Arm's instruction set for the chip's processing elements. This could pit Izanagi directly against NVIDIA's leadership position in AI chips. Son has a chest of $41 billion in cash at SoftBank that he can deploy for Izanagi.

Additionally, he is courting sovereign wealth funds in the Middle East to contribute up to $70 billion in additional capital. In total, Son may be seeking up to $100 billion to bankroll Izanagi into a chip powerhouse. AI chips are seeing surging demand as machine learning and neural networks require specialized semiconductors that can process massive datasets. NVIDIA and other names like Intel, AMD, and select startups have capitalized on this trend. However, Son believes the market has room for another major player. Izanagi would focus squarely on developing bleeding-edge AI chip architectures to power the next generation of artificial intelligence applications. It is still unclear if this would be an AI training or AI inference project, but given that the training market is currently bigger as we are in the early buildout phase of AI infrastructure, the consensus might settle on training. With his track record of bold bets, Son is aiming very high with Izanagi. It's a hugely ambitious goal, but Son has defied expectations before. Project Izanagi will test the limits of even his vision and financial firepower.

AMD today announced the signing of a definitive agreement to acquire Nod.ai to expand the company's open AI software capabilities. The addition of Nod.ai will bring an experienced team that has developed an industry-leading software technology that accelerates the deployment of AI solutions optimized for AMD Instinct data center accelerators, Ryzen AI processors, EPYC processors, Versal SoCs and Radeon GPUs to AMD. The agreement strongly aligns with the AMD AI growth strategy centered on an open software ecosystem that lowers the barriers of entry for customers through developer tools, libraries and models.

"The acquisition of Nod.ai is expected to significantly enhance our ability to provide AI customers with open software that allows them to easily deploy highly performant AI models tuned for AMD hardware," said Vamsi Boppana, senior vice president, Artificial Intelligence Group at AMD. "The addition of the talented Nod.ai team accelerates our ability to advance open-source compiler technology and enable portable, high-performance AI solutions across the AMD product portfolio. Nod.ai's technologies are already widely deployed in the cloud, at the edge and across a broad range of end point devices today."

UPMEM, a fabless semiconductor startup has raised €4.1 M equity from the European Innovation Council (EIC) Fund and Venture Capitalists (Partech, Western Digital Capital, C4 Ventures…), and a €2.5M grant from the EIC. Founded by Fabrice Devaux and Gilles Hamou, the company is pioneering ultra-efficient Processing In Memory (PIM) accelerators to tackle the significant challenge of compute efficiency for AI and big data applications.

UPMEM's PIM solution, integrating UPMEM's first commercial-grade PIM chip on the market, is now available to cloud markets across the globe (US, Asia...) to provide the most cost-effective and energy-efficient solutions for AI and analytics applications in data centers and at the edge, such as large language models (LLM e.g. GPT), genomics, large analytics.

In a major step to support India's industrial sector, NVIDIA and Reliance Industries today announced a collaboration to develop India's own foundation large language model trained on the nation's diverse languages and tailored for generative AI applications to serve the world's most populous nation. The companies will work together to build AI infrastructure that is over an order of magnitude more powerful than the fastest supercomputer in India today. NVIDIA will provide access to the most advanced NVIDIA GH200 Grace Hopper Superchip and NVIDIA DGX Cloud, an AI supercomputing service in the cloud. GH200 marks a fundamental shift in computing architecture that provides exceptional performance and massive memory bandwidth.

The NVIDIA-powered AI infrastructure is the foundation of the new frontier into AI for Reliance Jio Infocomm, Reliance Industries' telecom arm. The global AI revolution is transforming industries and daily life. To serve India's vast potential in AI, Reliance will create AI applications and services for their 450 million Jio customers and provide energy-efficient AI infrastructure to scientists, developers and startups across India.

Somewhat out of the blue, Silicon Box has announced the opening of its US$2 billion semiconductor assembly plant in Singapore. The "startup" is founded by several of Marvell's founders, suggesting the company has the right pedigree to compete in what is sure to be a very competitive market over the next few years. Silicon Box is not a foundry and will at least at this point in time, not be involved in foundry services, but instead the company will focus on advanced chip packaging technologies, focusing on chiplets.

The company is promising "faster time-to-market, lower new device design cost" on its very rudimentary website, something the company has yet to prove to be capable of. However, its new plant in Singapore covers 73,000 square metres and is said to feature state of the art production equipment for turning chiplets into chips. The factory is said to create some 1,200 jobs in Singapore, suggesting that this is a company that means business. According to a comment by company founder and CEO BJ Han to Reuters, "customers had been lining up" since before the completion of the assembly plant. Silicon Box is expecting to have several AI chipset companies as its customers, including Tenstorrent, which so far is the only officially mentioned client. Time will tell if Silicon Box can compete with established chip packaging businesses and if they can deliver on their promise to be faster and cheaper than the competition.

Three leading European generative AI startups joined NVIDIA founder and CEO Jensen Huang this week to talk about the new era of computing. More than 500 developers, researchers, entrepreneurs and executives from across Europe and further afield packed into the Spindler and Klatt, a sleek, riverside gathering spot in Berlin. Huang started the reception by touching on the message he delivered Monday at the Berlin Summit for Earth Virtualization Engines (EVE), an international collaboration focused on climate science. He shared details of NVIDIA's Earth-2 initiative and how accelerated computing, AI-augmented simulation and interactive digital twins drive climate science research.

Before sitting down for a fireside chat with the founders of the three startups, Huang introduced some "special guests" to the audience—four of the world's leading climate modeling scientists, who he called the "unsung heroes" of saving the planet. "These scientists have dedicated their careers to advancing climate science," said Huang. "With the vision of EVE, they are the architects of the new era of climate science."

Ethernovia, Inc., a Silicon Valley-based startup, today announced that it has completed its A-round financing of $64 million. The funding round consists of multiple investors, including Porsche Automobile Holding SE (Porsche SE), Qualcomm Ventures, VentureTech Alliance, AMD Ventures, Western Digital Capital, Fall Line Capital, Taiwania Capital, ENEA Capital and others.

As vehicle architectures evolve from domain-centric controllers, networking solutions must concurrently evolve to support higher data rates of advanced vehicle applications while meeting demand for improved reliability and security. Such advanced applications include Advanced Driver-Assisted Systems (ADAS), autonomous driving (AD) and a rich ecosystem of customer software delivered Over the Air (OTA).

Intel CEO Pat Gelsinger has issued the news, via a tweet, of Raja Koduri's departure from the silicon giant. Koduri, who currently sits as Executive Vice President and Chief Architect, will be leaving the company at the end of this month. This ends a five year long tenure at Intel, where he started as Chief Architect back in 2017. He intends to form a brand new startup operation that will focus on AI-generative software for computer games. His tweeted reply to Gelsinger reads: "Thank you Pat and Intel for many cherished memories and incredible learning over the past 5 years. Will be embarking on a new chapter in my life, doing a software startup as noted below. Will have more to share in coming weeks."

Intel has been undergoing numerous internal restructures, and Koduri's AXG Graphics Unit was dissolved late last year. He was the general manager of the graphic chips division prior to its split, and returned to his previous role as Chief Architect at Intel. The company stated at the time that Koduri's new focus would be on: "growing efforts across CPU, GPU and AI, and accelerating high-priority technical programmes."