Artificial Intelligence in Hardware

After a decade at Intel, I learned something that will blow your mind about the semiconductor industry. The $600B chip market just changed forever. Here's why: → Generic chips are hitting a wall → AI workloads need custom silicon → One-size-fits-all is dead. But Broadcom + OpenAI just revealed the solution: CUSTOM AI CHIPS. • Tesla's FSD chip: 21x faster than GPUs • Google's TPUs: 80% cost reduction • Apple's M-series: 40% better efficiency • Amazon's Graviton: 20% price improvement Instead of forcing AI into generic hardware... what if we built hardware specifically for AI? The benefits are insane: - 10x performance improvements - 50% power reduction - Custom architectures for specific models - Direct chip-to-algorithm optimization - Massive cost savings at scale This is about RETHINKING THE ENTIRE STACK. From my manufacturing AI work, I've seen how custom silicon transforms production lines. Now we're seeing the same revolution in AI infrastructure. Sometimes the best solutions hide in plain sight 🌟 #AI #Semiconductors #Innovation #Manufacturing #TechTrends #DigiFabAI

AI Agents are driving a new compute race. And companies are batting on a different path… The next generation of AI Agents, reasoning models, and enterprise AI systems will rely on an entire ecosystem of specialized processors working together. And that's exactly why companies like NVIDIA, Google, AMD, Apple, Qualcomm, and Groq are all taking different approaches to AI hardware. 📌 The reason is simple: Different AI workloads have different requirements. Training a frontier model is very different from running an AI Agent on your laptop. A real-time voice assistant has different constraints than a data center serving millions of users. This is why the future AI stack is becoming increasingly heterogeneous. Let me break it down: 📌 CPU (Central Processing Unit) * Handles orchestration, scheduling, and control flow. * Manages operating systems, applications, and AI infrastructure. * Acts as the coordinator for other processors. Examples: Intel Xeon, AMD EPYC 📌 GPU (Graphics Processing Unit) * Designed for massive parallel computation. * Powers most modern AI training and large-scale inference. * The foundation of today's AI boom. Examples: NVIDIA H100/Blackwell, AMD MI300X 📌 TPU (Tensor Processing Unit) * Built specifically for tensor operations. * Optimized for large-scale machine learning workloads. * Commonly used across Google's AI ecosystem. Examples: Google TPU v5e/ v6 📌 NPU (Neural Processing Unit) * Brings AI directly onto devices. * Optimized for power-efficient inference. * Enables AI PCs, smartphones, and edge computing. Examples: Apple Neural Engine, Qualcomm Hexagon, Intel AI Boost 📌 LPU (Language Processing Unit) * Designed specifically for language model inference. * Focuses on low latency and high token generation speed. * Ideal for real-time AI applications. Examples: Groq LPU 📌 DPU (Data Processing Unit) * Handles networking, security, and data movement. * Offloads infrastructure tasks from CPUs. * Increasingly important in AI data centers. Examples: NVIDIA BlueField, AMD Pensando 📌 So why are all hardware companies pursuing different strategies? Because there is no single "best" compute for AI. NVIDIA is focused on AI acceleration. Google is optimizing for tensor workloads. Apple and Qualcomm are pushing AI to the edge. Groq is targeting ultra-fast inference. AMD is building alternatives across the AI infrastructure stack. Each company is solving a different bottleneck. And that's why the future won't be GPU-only. The future AI stack will combine CPU, GPU, TPU, NPU, LPU, and DPU working together to power increasingly capable AI systems. I created this visual to simplify categories shaping modern AI infrastructure. 📌 If you want to go deeper on AI Agents, please join my free newsletter https://lnkd.in/esJDdA5Q Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents #AIAgents #AIInfrastructure #AcceleratedComputing #EnterpriseAI #MachineLearning #NVIDIA

If you think AI = GPUs, you're missing 80% of the story — Here’s the infrastructure stack that makes AI real Everyone talks about GPUs, TPUs, HBM, and advanced nodes. But AI data centers are massive, tightly engineered systems where *infrastructure* decides what performance is actually achievable. Here’s what really powers AI at scale: → Medium voltage power distribution   This is the backbone. It delivers massive, stable power to AI facilities running extreme loads 24/7. → Backup power   AI workloads cannot afford downtime. Redundant generators and power systems keep models running during outages. → Uninterruptible power systems (UPS)   Millisecond level protection that prevents crashes, data loss, and hardware damage. → Building automation   Real time control of power usage, airflow, cooling efficiency, and operational safety. → Security systems   Physical and digital protection for some of the most valuable compute assets on Earth. → HVAC and thermal management   AI racks generate extreme heat. Cooling is now one of the hardest engineering problems in data centers. → Server cabinets and racks   Designed for density, airflow, cable management, and fast deployment of high power systems. → Low voltage power distribution   The final layer that safely delivers power to every rack, server, and subsystem. Key takeaway:   AI is not just a semiconductor story.   It’s an infrastructure story. Power, cooling, automation, and reliability are what turn silicon into real world AI capability. Which layer do you think becomes the biggest bottleneck as AI scales? Follow me Ali Kamaly for more posts like this.   And check out our blog The Semiconductor World — link in the comments. #AI #DataCenters #Semiconductors #Infrastructure #DeepTech

AI Won't Replace Chip Engineers. It Will Replace Chip Engineers Who Don't Understand AI. A few years ago, students asked: "Will AI replace chip engineers?" Today, that’s the wrong question. The real question is: What kind of chip engineer becomes replaceable in the AI era? AI will not replace engineers who understand systems, constraints, and silicon accountability. But it may replace engineers who only follow tool steps without understanding why they matter. Here’s what’s happening: Global semiconductor sales hit $298.5B in Q1 2026, up 25% in three months. The market is projected to reach $1 trillion this year, with nearly half tied to generative AI chips. AI is driving demand for more compute, memory bandwidth, advanced packaging, and power efficiency. AI may look like software from the outside. But at scale, AI becomes a chip design problem. Companies like Synopsys and Cadence already use AI for RTL generation, design optimization, debugging, verification workflows, and DFT automation. AI can explore design spaces faster. But faster exploration does not equal correct engineering. That’s where human judgment still matters. Here's what AI does well: → Generating starter RTL and testbench skeletons → Summarizing simulation logs → Suggesting debug paths → Automating DFT analysis → Exploring PPA optimization faster Here's what AI still struggles with: 1. Engineering Judgment. AI can suggest optimizations. It cannot fully balance timing, power, area, and system tradeoffs. 2. Constraints Thinking. AI can follow rules. Engineers understand why those rules exist. 3. Verification Meaning. Generating more tests is not the same as asking the right failure questions. 4. Silicon Accountability. AI doesn’t sign off on chips. Engineers do. The industry is not eliminating chip engineering roles. It’s evolving them. Future-proof engineers will combine: → Hardware fundamentals → Verification thinking → DFT awareness → Python automation → AI understanding from a hardware perspective The students who win won’t avoid AI. They’ll understand where AI helps, where it fails, and where human engineering judgment still matters. So the better question isn’t: "Will AI take my job?" It’s: "Am I learning the kind of engineering AI cannot fake?" That’s where the future begins. Want the full deep dive? This is a summary of our latest Substack newsletter covering AI-assisted chip design, career mapping, and why silicon accountability still can’t be automated. Read the full article on Substack: https://lnkd.in/gYJzuKPY #SemiconductorEngineering #ChipDesign #AIinEngineering #EDA #VLSI #CareerRoadmap #STEM #AItoSilicon

𝐖𝐡𝐲 𝐍𝐕𝐈𝐃𝐈𝐀 𝐎𝐰𝐧𝐬 𝐭𝐡𝐞 𝐀𝐈 𝐄𝐫𝐚 𝐓𝐡𝐞 𝐒𝐢𝐥𝐞𝐧𝐭 𝐒𝐡𝐢𝐟𝐭 𝐁𝐞𝐡𝐢𝐧𝐝 𝐌𝐨𝐝𝐞𝐫𝐧 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 AI feels like software progress. In reality, it is a hardware revolution hiding in plain sight. → 𝐀𝐈 𝐈𝐬 𝐍𝐨𝐭 𝐋𝐨𝐠𝐢𝐜. 𝐈𝐭 𝐈𝐬 𝐌𝐚𝐭𝐡 • Modern AI runs on massive matrix multiplication • Trillions of parallel calculations per second • Performance depends on arithmetic speed and data flow, not clever code → 𝐖𝐡𝐲 𝐂𝐏𝐔𝐬 𝐅𝐞𝐥𝐥 𝐁𝐞𝐡𝐢𝐧𝐝 • Designed for decision making and branching • AI workloads are predictable and repetitive • Control logic and memory bottlenecks limit scale → 𝐆𝐏𝐔𝐬 𝐂𝐡𝐚𝐧𝐠𝐞𝐝 𝐭𝐡𝐞 𝐆𝐚𝐦𝐞 • Thousands of simple cores working together • One instruction executed across massive data sets • Built for throughput, not decision complexity → 𝐌𝐞𝐦𝐨𝐫𝐲 𝐈𝐬 𝐭𝐡𝐞 𝐑𝐞𝐚𝐥 𝐁𝐚𝐭𝐭𝐥𝐞𝐟𝐢𝐞𝐥𝐝 • Models are larger than compute cost • Every AI request touches the full model • Bandwidth, not clocks, defines speed → 𝐍𝐕𝐈𝐃𝐈𝐀’𝐬 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐚𝐥 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 • Tensor Cores purpose built for matrix math • High Bandwidth Memory placed next to the chip • CUDA creates an end to end AI software moat → 𝐓𝐡𝐞 𝐁𝐢𝐠𝐠𝐞𝐫 𝐒𝐡𝐢𝐟𝐭 • Computing moved from if then logic to probabilistic transformation • Silicon architecture is now a strategic asset • AI leadership is decided at the hardware level Follow Umair Ahmad for more insights

AI isn’t just a software revolution, it’s a hardware revolution, maybe the biggest since the dawn of computing. I’m with Jensen Huang on this: this is the first true reinvention of computing architecture in 60 years. What’s thrilling? Technologies we once shelved as “too early” or “too exotic” are roaring back because AI demands it: ̇ᐧ Optical computing → Celestial AI, Lightmatter, LightOn, and others reviving light-based processors to break energy barriers. ᐧ Neuromorphic computing → Intel’s Loihi and IBM’s TrueNorth mimic brain-like networks for ultra-efficient learning. ᐧ Quantum computing → IBM, Google, and Rigetti are chasing quantum acceleration — once niche research, now seen as a potential leap for AI optimization, quantum ML, and beyond. ᐧ Silicon photonics & new materials → Ayar Labs and others push past electronic limits using light-speed interconnects. ᐧ Advanced packaging → Intel, TSMC, and Samsung race to stack and stitch chips together to feed insatiable AI workloads. AI isn’t just pushing hardware, it’s forcing us to open the vault and reimagine what a computer even is. This is the biggest hardware shift in decades. Are you ready to build for it? #AIHardware #Neuromorphic #JensenHuang #FutureOfComputing #EngineeringInnovation #NextGenChips

While much of the AI conversation revolves around models, agents, and applications, the real strategic race is increasingly moving down the stack. The UK's new $1.5 billion AI Hardware Plan is a recognition of a simple reality: nations that control compute, chips, and AI infrastructure will have a disproportionate influence on the next wave of technological and economic growth. A few things stand out: • £750 million for a national AI supercomputer • Direct government commitments to purchase next-generation AI chips from startups • Dedicated funding for AI hardware innovation and semiconductor talent • A major effort to attract private capital into British hardware companies What's particularly interesting is the focus on inference hardware. As AI adoption scales globally, inference will become one of the largest infrastructure markets in technology. The companies that make AI cheaper, faster, and more energy efficient will create enormous value. For years, software captured most of the attention. The next decade could see hardware become one of the most important competitive battlegrounds in AI. The countries investing early in sovereign compute, semiconductor innovation, and AI infrastructure are positioning themselves for long-term leadership. The UK just made it clear it intends to be one of them. https://lnkd.in/gXYPq4iN

CPU-exposed AI names just had a sharp re-rating. Intel up more than 50% in 30 days. Arm up nearly 90% YTD. AMD rallying with the group. This is not just a PC refresh story. Part of the market is repricing a hardware ratio many 2025 AI plans underweighted. Training-era infrastructure was optimized for feeding GPUs. Agentic infrastructure has to optimize for completing loops. GPU-first AI infrastructure: ~30M CPU cores per gigawatt. CPU-to-GPU ratio of 1:4 to 1:8. Agentic AI infrastructure: ~120M CPU cores per gigawatt. Ratio shifting toward 1:1–1:2. (Arm / TrendForce estimates. Directional, not physical law.) 4x more CPUs per GW. That is the structural shift getting repriced. 𝗪𝗵𝘆 — 𝗳𝗿𝗼𝗺 𝗳𝗶𝗿𝘀𝘁 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀: ▸ Traditional software runs control flow designed ahead of time. Deterministic path. ▸ Agentic software selects part of the control flow at runtime. The agent runtime picks the next tool, retry, or branch based on model output and policy. ▸ GPUs are parallel accelerators optimized for dense tensor math — token generation, embeddings, matrix ops. Poor fit for branchy, stateful, sequential control logic. ▸ CPUs shoulder most of the agent-control work: context assembly, JSON parsing, tool routing, auth, retrieval, DB I/O, verification, retries, state updates. Training and batch inference are still GPU-led. The shift shows up in interactive, tool-heavy, stateful workloads. 𝗧𝗵𝗲 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝘁𝗵𝗮𝘁 𝗱𝗿𝗶𝘃𝗲𝘀 𝗰𝗮𝗽𝗲𝘅: After the plan, the workflow shifts to CPU-heavy orchestration: tool calls, I/O, state updates. In profiled agent workloads, time outside the model can dominate end-to-end latency. Without good scheduling, the accelerator allocated to that workflow waits. An idle accelerator at hyperscaler prices is a P&L problem, not a systems detail. Moving state across PCIe also costs latency and Joules. Coherent-memory systems like NVIDIA's GH200 (Grace + Hopper over NVLink-C2C) attack that directly. Other signals point to the same shift: Intel Xeon 6 selected as host CPU for NVIDIA's DGX Rubin NVL8, NVIDIA shipping Vera CPU standalone (per NVIDIA, "purpose-built for agentic AI"). Host-side orchestration is now strategic infrastructure. 𝗙𝗼𝗿 𝗔𝗜 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻: ▸ Stop budgeting on tokens per second. Baseline cost per completed agent task — retries included. ▸ Track GPU idle time during agent runs. On stateful workloads, idle accelerators signal orchestration is the real bottleneck. ▸ Evaluate coherent-memory topologies (Grace-Hopper, MI300A) when host-side orchestration or memory movement gates throughput. ▸ Plan CPU procurement 2–3 quarters out. Lead times have stretched from weeks to months. Intelligence is generated on GPUs. Agentic intelligence is orchestrated on CPUs. Before you approve the next GPU cluster, measure one number: what percentage of task time happens outside the model? #AIInfrastructure #AgenticAI #LLMOps #SystemsEngineering

Been reading a lot lately about GPUs not being enough, pauses in new GPU releases, and the rise of custom silicon like TPUs and in-house AI accelerators. Zooming out, this feels like a particularly exciting time to be in RTL design and ASIC development. What often gets less attention is that as AI scales, networking ASICs become just as critical. Moving data between accelerators, memory, and racks fast, efficiently, and at scale is now a first-order problem. Compute doesn’t matter if the data can’t get there in time. Between AI accelerators and high-performance networking silicon, we’re seeing real architectural diversity again. Decisions around memory systems, interconnects, data movement, and power efficiency are shaping the future of computing. I’m especially excited to dig deeper into whitepapers on these architectures and understand the tradeoffs behind them. Feels like one of those moments where being close to silicon gives you a front-row seat to how computing is evolving. As AI systems scale, do you think the next big bottleneck will be compute, memory, or the network connecting it all? #ASICDesign #RTLDesign #NetworkingASICs #AIHardware #Silicon #ChipDesign #ComputerArchitecture #HardwareEngineering #DataCenterNetworking