AI Hardware Innovations Overview

The Future of AI Hardware: How Chiplets and Silicon Photonics Are Breaking Performance Barriers As AI computing demands soar beyond the limits of traditional semiconductor technology, heterogeneous integration (HI) and Silicon Photonics are emerging as the next frontier in advanced packaging. The shift toward chiplet-based architectures, Co-Packaged Optics (CPO), and high-density interconnects unlocks higher performance and greater energy efficiency for AI and High-Performance Computing (HPC) applications. ASE, a leading Outsourced Semiconductor Assembly and Test provider based in Kaohsiung, Taiwan, is pioneering advanced packaging solutions like 2.5D & 3D ICs, FOCoS, and FOCoS-Bridge to optimize bandwidth, reduce power consumption, and enhance AI and HPC performance through heterogeneous integration and Co-Packaged Optics (CPO). AI systems will require ExaFLOPS computing power, potentially integrating millions of AI chiplets interconnected through photonics-driven architectures. As the industry rallies behind CPO, innovations in fiber-to-PIC assembly, wafer-level optical testing, and known-good optical engines (OE) will define the future of AI infrastructure. My Take AI hardware is no longer just about faster chips—it’s about smarter packaging.  Photonic integration and chiplet-based architectures aren’t just theoretical breakthroughs; they’re the key to keeping AI performance scalable and sustainable. The companies that master high-density interconnects and efficient optical coupling will dominate the AI era. #AIHardware #Chiplets #SiliconPhotonics #CoPackagedOptics #HPC #AdvancedPackaging #DataCenterTech #AIComputing #Semiconductors Link to article: https://lnkd.in/ezgCixXy Credit: Semiconductor Engineering This post reflects my own thoughts and analysis, whether informed by media reports, personal insights, or professional experience. While enhanced with AI assistance, it has been thoroughly reviewed and edited to ensure clarity and relevance. Get Ahead with the Latest Tech Insights!  Explore my searchable blog: https://lnkd.in/eWESid86

Researchers have made a significant breakthrough in AI hardware with a 3D photonic-electronic platform that enhances efficiency and bandwidth, potentially revolutionizing data communication. Energy inefficiencies and data transfer bottlenecks have hindered the development of next-generation AI hardware. Recent advancements in integrating photonics with electronics are poised to overcome these challenges. 💻 Enhanced Efficiency: The new platform achieves unprecedented energy efficiency, consuming just 120 femtojoules per bit. 📈 High Bandwidth: It offers a bandwidth of 800 Gb/s with a density of 5.3 Tb/s/mm², far surpassing existing benchmarks. 🔩 Integration: The technology integrates photonic devices with CMOS electronic circuits, facilitating widespread adoption. 🤖 AI Applications: This innovation supports distributed AI architectures, enabling efficient data transfer and unlocking new performance levels. 📊 Practical Quantum Advancements: Unlike quantum entanglement for faster-than-light communication, using quantum physics to boost communication speed is more feasible and practical. This breakthrough is long overdue, but the AI boost might create a burning need for this technology. Quantum computing might be seen as a lot of hype, but using advanced quantum physics to enhance communication speed is more down-to-earth than relying on quantum entanglement for faster-than-light communications, which is short-lived #AI #MachineLearning #QuantumEntanglement #QuantumPhysics #PhotonicIntegration #SiliconPhotonics #ArtificialIntelligence #QuantumMechanics #DataScience #DeepLearning

𝗧𝗟;𝗗𝗥: As AI evolves especially with LLMs, the hardware necessary to train and use them (inference) is also evolving fast. While GPUs are the foundation for AI models today, there are several hardware options emerging that could complement or even replace GPUs. 𝗚𝗣𝗨𝘀 have been the key enabler for AI models since Alexnet in 2012 (https://lnkd.in/eCQ8C7FW). 𝘘𝘶𝘪𝘤𝘬 𝘳𝘦𝘮𝘪𝘯𝘥𝘦𝘳 𝘸𝘩𝘺 𝘎𝘗𝘜𝘴 𝘢𝘳𝘦 𝘨𝘰𝘰𝘥 𝘧𝘰𝘳 𝘕𝘦𝘶𝘳𝘢𝘭 𝘕𝘦𝘵𝘸𝘰𝘳𝘬𝘴: 𝘩𝘵𝘵𝘱𝘴://𝘣𝘪𝘵.𝘭𝘺/3𝘈𝘙8𝘺𝘝𝘣. NVIDIA has been advancing GPUs rapidly but GPUs were not designed originally for AI use cases! Recent innovation (and investment) has focused on improving speed and lowering cost using AI focused chips and systems which will be focus of this post. But, first some basics on LLMs inferencing. 𝗟𝗟𝗠𝘀 are a unique class of AI models – They are 1/Very large (Billions of parameters) and hence need lots of memory and 2/Autoregressive which means for each word generation 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝗟𝗟𝗠 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗽𝘂𝗹𝗹𝗲𝗱 𝗳𝗿𝗼𝗺 𝗺𝗲𝗺𝗼𝗿𝘆 𝘁𝗼 𝗚𝗣𝗨 which requires massive memory bandwidth. I wrote about this earlier: https://bit.ly/4dOuUFa. So how do you speed up model inference while lowering cost? Lets see how some do it: Cerebras Systems Inc. – Cerebras is one of the fastest AI hardware systems today (445 tokens per sec for Llama 3.1 70B). 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: They just 𝗯𝘂𝗶𝗹𝗱 𝗯𝗶𝗴 𝗰𝗵𝗶𝗽𝘀 𝗮𝗻𝗱 house 𝗺𝗲𝗺𝗼𝗿𝘆 (𝘄𝗵𝗲𝗿𝗲 𝗺𝗼𝗱𝗲𝗹 𝗶𝘀 𝘀𝘁𝗼𝗿𝗲𝗱) 𝗰𝗹𝗼𝘀𝗲𝗿 𝘁𝗼 𝘁𝗵𝗲 𝗚𝗣𝗨, with the memory bandwidth at a staggering 21 Petabytes/s which is 7000x of the Nvidia H100! Some incredible hardware engineering.  𝗠𝗼𝗿𝗲 𝗵𝗲𝗿𝗲: https://bit.ly/3ZaxEbG Groq – 7 year old Groq found great product market fit in the last year & been doing some great work.   𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Groq is all about a (very) smart 𝗰𝗼𝗺𝗽𝗶𝗹𝗲𝗿 combined with a 𝘀𝗶𝗺𝗽𝗹𝗲 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 architecture with 𝗻𝗼 𝗸𝗲𝗿𝗻𝗲𝗹! Using very advanced 𝗗𝗮𝘁𝗮𝗳𝗹𝗼𝘄 architecture they can map out when an execution needs to be computed all on a deterministic compute layer. Groq afaik does not have a lot of on-chip memory which means for large models they will need LOTS of chips/racks but its all abstracted via Groq Cloud run by the awesome Sunny Madra and team.  𝗠𝗼𝗿𝗲 𝗵𝗲𝗿𝗲: https://bit.ly/3ZhNnFE. SambaNova Systems – SambaNova recently announced really fast throughput 114Tps for the large Llama 405B model   𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 – They are also a DataFlow architecture but they have memory on board which allows them to support larger models with (potentially) fewer chips & racks.  𝗠𝗼𝗿𝗲 𝗵𝗲𝗿𝗲: https://bit.ly/3XtbUGD Of course Amazon Web Services (AWS) has Trainium and Inferentia.  𝗠𝗼𝗿𝗲 𝗵𝗲𝗿𝗲: https://bit.ly/47gfiIo Many more good companies like d-Matrix, Tenstorrent, Etched in this space. 𝗔𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗧𝗢𝘀, 𝗖𝗔𝗜𝗢𝘀: Have a GPU diversification strategy to reduce risk, cost and improve perf!

The AI ‘arms race’ has been dominated by compute hardware but competing for the fastest chip is a ‘zero sum’ game - one winner and many losers. The idea that one company (ahem, NVIDIA) could dominate the full stack goes against the very idea of AI being democratic, and I just don’t support that. AI is an ecosystem and I’m striving to highlight the companies developing ENABLING technologies that make hardware work at scale. The solution isn’t more power— it’s greater efficiency. This means solving memory bottlenecks, interconnectivity, network fabrics, and model optimization. These are some of the innovators making the biggest impact: DIMC/NMC Breaking the Memory Wall Most AI models are bottlenecked by memory bandwidth and some think inference–the next big leap in AI–will be made efficient with in-memory or near-memory compute. Others contest these approaches only provide marginal gains, but d-Matrix and Untether AI have proven otherwise. Let’s see if Sam Altman supercharges Rain AI because they’ll drive advancement, too. Next-Gen Network Fabrics DC operators live and die by hardware utlization which requires bulletproof network fabrics. This space was primed for disruption, and AI was it. Companies building next-gen interconnect devices that mesh GPUs and reduce system complexity will transform this market and DC operators are praising Enfabrica and Astera Labs for being pioneers in an oft-overlooked but critical space. Hardware-Software Optimization Advanced AI models are moving away from brute-force training toward greater efficiency using approaches like Mixture of Experts (DeepSeek AI broke the mold). Companies are paying more attention to optimizaing their stacks and Baya Systems and Lemurian Labs are creating those links so customers can get the most out of hardware without being held CUDA-captive. Custom AI Silicon Scalability limits of traditional architectures and time-to-market is brutal in the AI era. You can’t afford to work on a chip for two years while competitors leapfrog you. RISC-V is challenging proprietary ISAs like x86 and ARM by enabling chips purpose-built for AI, and customers also keep greater control over their silicon. Tenstorrent, Rivos Inc., and Akeana are at the forefront of this shift. Photonics Interconnectivity and Compute Mark Wade has said, “copper’s time is up” and photonics is changing the way datacenters approach interconnectivity. Ayar Labs and Lightmatter have compelling theses, however, the market still needs persuasion to make sweeping infrastructure changes. Deeper still, Neurophos is using light as a first principle, leveraging it for AI compute. The AI hardware gold rush has led to massive GPU investments but again, the real value for investors and engineers is in the ecosystem. Enabling AI to scale, become useful, and democratic is far more impactful than trying to drive a stock up by any means necessary. I welcome any debate about that. #semiconductorindustry #artificialintelligence #startups