James Drury’s Post

The Future of AI and Semiconductor Demand The rise of artificial intelligence (AI), particularly in the field of generative AI, is driving an unprecedented demand for high-performance semiconductors. Unlike previous technology cycles focused on personal computers and mobile devices, the unique computational needs of AI are leading to significant advancements in hardware and manufacturing techniques. This demand surge is a central aspect of the "AI Supercycle," with global semiconductor revenues projected to exceed $1 trillion by 2030. Key Factors Driving Demand: 1. Training and Inference: - Training: This phase of AI development requires immense parallel processing power to manage vast datasets and complex algorithms. Graphics Processing Units (GPUs), led by NVIDIA, are essential for this processing capability. - Inference: This phase involves making real-time predictions based on trained models, necessitating exceptionally fast processing and increasing the demand for specialized inference chips, particularly for generative AI applications. 2. Hyperscale Data Centers: These large facilities are critical for cloud computing, providing the infrastructure needed for AI applications. Major tech companies, including Microsoft, Google, and Amazon, are expanding and redesigning their data centers to meet the high computational demands of AI workloads. 3. Specialized Chips and Memory: As AI workloads become more complex, there is a shift from traditional general-purpose Central Processing Units (CPUs) to specialized processors designed for specific tasks. - AI Accelerators: This category includes Tensor Processing Units (TPUs), Field Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), all optimized for specific AI tasks. - High-Bandwidth Memory (HBM): The need for rapid data processing and high data throughput is making HBM increasingly important, enhancing the capabilities of AI processors. In summary, the convergence of AI advancements and semiconductor technology is creating a future filled with opportunities and innovations that are set to transform industries. #MicronTechnology #hbm #ai #semiconductor

⚡ AI may no longer belong to digital computing. Recent breakthroughs suggest the future of AI could be analog. 🔹 Microsoft just unveiled an analog optical computer that processes continuous signals instead of binary bits. Result? 100x efficiency gains compared to today's most advanced GPUs. 🔹 Indian Institute of Science (IISc) researchers developed an analog in-memory platform with 16,500 conductance states. Digital systems are limited to just 2 states (0 & 1). 👉 The key breakthrough: processing data directly inside memory. No more shuttling data back and forth between processors and memory-a major source of energy waste in digital systems. 🌍 Why this matters: Ultra-efficient AI on devices → Models could run on your phone, not just cloud supercomputers Massive energy savings → Cuts the staggering costs of training & inference Real-time intelligence → Instant AI responses become the default Industry impact → Healthcare, finance, edge devices, and autonomous systems get smarter, faster, and greener This isn't just about speed. It's about sustainability and democratization. Analog's continuous spectrum is like moving from a light switch (0/1) to a dimmer with infinite shades. Of course, challenges remain. Scaling prototypes into full production systems is tough. But early results show we're much closer than expected. 💡 History shows every paradigm shift comes with skepticism-yet once scalability is solved, entire industries transform. From vacuum tubes → transistors → microchips... now analog AI may be the next leap. The trillion-dollar question: Are we witnessing the start of AI's post-digital era? #AnalogComputing #AI #MachineLearning #ArtificialIntelligence #SustainableAI #FutureOfAI #DeepLearning #AIHardware #EdgeAI #NeuromorphicComputing #Innovation #TechBreakthrough #EnergyEfficiency #DataCenters #AIProcessing #InMemoryComputing #QuantumVsAnalog #DigitalTransformation #GreenTech #IISc #MicrosoftResearch Source: https://lnkd.in/dJ3B_fqq

UK-based company Oxford Quantum Circuits just activated NYC’s first quantum computer, designed to accelerate AI training and improve energy efficiency.

Chinese scientists create analogue AI chip that can perform AI task 3,000 times faster than Nvidia’s A100: study NVIDIA #Newsvidia #Nvidia #NvidiaNews #AI https://lnkd.in/d7RHNtxa

The biggest threat to tech is tech - Microsoft to use Microfluidics to Cool AI Chips Instead of external cooling, there will be fluid in tiny channels etched into the chips Quantum computing, Underwater/space data centers, 200 layers on chips, Humanoids What a time to be alive!

🧠 Watching the Rise of Brain-Inspired Machines We’re witnessing the next great rewiring of artificial intelligence. Neuromorphic computing — AI modeled after the human brain — is moving from theory to tangible hardware. Chips like Intel’s Loihi 2 and IBM’s NorthPole are no longer experiments; they’re proof that AI can think, learn, and adapt using a fraction of the power that today’s GPUs demand. As I watch this unfold, it’s clear we’re entering a phase where AI won’t just compute — it will sense and respond. By merging memory and processing, these chips dissolve the old Von Neumann bottleneck, processing data like neurons firing in real time. That means smarter edge devices, energy-conscious AI, and perhaps, a first step toward machines that genuinely understand context. This isn’t just new tech—it’s a paradigm shift. The convergence of neuroscience, physics, and AI will birth systems that don’t imitate intelligence… they embody it. — Claude Edwin Theriault AI Strategist | Watching the Brain’s Blueprint Redefine the Machine #NeuromorphicComputing #AIHardware #ClaudeEdwinTheriault #GenerativeAI #FutureTech #BrainInspiredAI

What if AI could run thousands of times faster while using a fraction of the energy? Critical for small nations such as #Bermuda which has the highest energy costs in the world. A new paper in Nature Computational Science introduces an analog in-memory computing attention mechanism that could completely transform how large language models (LLMs) are deployed. Instead of relying on GPUs that constantly reload massive caches of data, this breakthrough uses gain-cell memory arrays to both store and compute attention in place. The results are striking: • 7,000× faster than some GPUs • 90,000× lower energy use Why does this matter? Because the future of AI shouldn’t only belong to those with endless GPU clusters. Countries like Bermuda, and many others without hyperscale datacenters, could access ultrafast, low-power AI that runs locally, efficiently, and sustainably. This is more than an academic result — it’s a glimpse at how AI can scale globally without scaling energy costs out of control. #ai #energy https://lnkd.in/eFiDv8_P

The energy requirements of current AI is prohibitive, the solution is coming in the form of Quantum Computing AI is guzzling electrons and the bill is arriving early. Data centres are chasing power. Grids are gasping. Adding more GPUs is not a strategy. It is an admission that we have not thought hard enough about the workload. Enter quantum. Classical compute sprints in straight lines. Annealing quantum systems explore the maze and settle near the optimum. Let AI sketch the map. Let quantum pick the path. 1) Site the iron, steer the electrons Use quantum optimisation to choose where to build and how to feed it. Co optimise land, latency, cooling, tariffs, and transmission in one model. When the lights matter, good enough is not enough. 2) Do less work, get better answers Offload the knottiest combinatorial work to annealers and pair QPUs with GPUs. The greenest joule is the one you never spend. Hybrid stacks turn brute force into informed force. 3) Build leaner models with quantum aware workflows Use annealing inside the pipeline for feature selection, architecture search, scheduling, and simulation heavy domains. The result is less waste in training, tighter inference, and fewer megawatts per marginal gain. The practical move Energy is the constraint that will discipline AI. The fix is not another shed of accelerators. The fix is changing the workload. Start here: Optimise siting and grid operations with quantum in the loop Hybridise compute and give QPUs the combinatorics Make AI pipelines quantum aware and measure watts per win Developer bridges already exist, so engineers can call quantum like any other accelerator. No mystique. Just lower power and better answers. #QuantumAnnealing #QuantumAI #QPUs #HybridCompute #AIInfrastructure #SustainableAI #GridOptimisation #ANZTech

Next-Generation Chips are driving the semiconductor evolution with AI accelerators, neural networks, and chiplet architectures at the forefront. What you should know: - The theme examines impacts across the semiconductor value chain including key players like ASML, TSMC, Nvidia, and Cerebras. - Major trends include AI accelerators, neural network compute, chiplet architectures, plus pressures from tech nationalism, energy use, and skills shortages. - Key challenges: scaling advanced nodes, geopolitics & trade tensions (especially US-China), rising energy demands, and concentration of suppliers. 👉 Click here to request free sample pages: https://lnkd.in/dNaQkSKu        #GlobalData #Semiconductors #NextGenChips #AIHardware #TechTrends

🚀 Cornell’s “Microwave Brain” Chip: Redefining Computing and AI Imagine a chip that can decode radio signals, track radar, and analyze data in real-time—all while consuming less than 200 milliwatts. Cornell University researchers have made this a reality with their “microwave brain” processor. 👉 What’s revolutionary? 🔹First fully functional microwave neural network on a silicon chip 🔹Operates in the analog microwave range, processing data streams at tens of gigahertz 🔹Achieves 88%+ accuracy on wireless signal classification while using a fraction of power and space compared to digital processors 🔹Capable of hardware security applications and edge AI deployment on devices like smartwatches or phones 💡 Why it matters: This chip bypasses conventional digital signal processing steps, offering ultra-fast, energy-efficient, and scalable computation. It’s a glimpse into a future where AI can run locally, securely, and with minimal power—a game-changer for edge computing and smart devices. Cornell’s microwave brain proves that rethinking conventional circuit design can unlock new horizons in computing. #AI #EdgeComputing #Innovation #MicrowaveChip #CornellUniversity #NeuralNetworks #LowPowerComputing #TechBreakthrough 📖 Dive deeper via DeepTech Bytes: https://lnkd.in/gfU86gMe