Lauro Rizzatti Contrast 20 watts with a megawatt: The brain and the LLM aren’t in the same race. Every time a large language model (LLM) answers a question, it does something the human brain has done effortlessly for 300,000 years, but it does so by burning enough electricity to power a small neighborhood. The brain in our … Read more
Predictive, generative, and agentic AI are not successive generations. They coexist inside enterprise systems, each placing fundamentally different demands on the underlying compute infrastructure. The enterprise context layer A consumer chatbot relies primarily on pretraining weight memory and optional web search. An enterprise system must operate against information absent from pretraining: engineering specifications, financial ledgers, … Read more
For the past decade, the artificial intelligence landscape followed a remarkably simple recipe: if you wanted better AI, you built larger models, fed them more data, and added more GPUs. The formula worked, and it worked spectacularly. Every new generation of hardware enabled larger neural networks, and larger neural networks broadened their reach. What began … Read more
In the early 1980s, when computer-aided engineering (CAE), the precursor to modern electronic design automation (EDA), was just taking shape, my professional trajectory shifted in a way that would prove foundational. I joined Teradyne, the Boston-based leader in automated test equipment (ATE), and I encountered for the first time the world of logic and fault … Read more
For the past decade, the semiconductor industry has been moving in one direction: shift-left, specifically, shifting more validation into the pre-silicon phase. The idea was straightforward: if software ultimately determines how a system behaves, then software should become a primary vehicle for verification. The industry responded by investing heavily in virtual platforms, emulation systems, and … Read more
Any discussion of modern AI system performance must include MLCommons and its MLPerf benchmark suite, which has become the industry’s de facto standard for measuring machine learning performance. Since its debut in 2018, MLPerf has provided a neutral, peer-reviewed framework for comparing hardware and software platforms across a broad range of AI workloads. The original … Read more
Recent frontier LLM inference benchmarks have highlighted a recurring pattern. GPU-based systems deliver outstanding throughput when latency is not a concern, but their performance drops sharply once real-time response requirements are imposed. This behavior is sometimes attributed to software inefficiencies or suboptimal system tuning. In reality, the root cause lies much deeper. It reflects a … Read more
There is a growing architectural tension at the heart of modern AI infrastructure. The processors that enabled the deep learning revolution—graphics processing units (GPUs)—remain the dominant engines of large-scale training and inference. Yet the computational profile of frontier language models is evolving in ways that increasingly expose the structural assumptions embedded in GPU design. Memory … Read more
Hardware emulation arose as a necessity out of the needs of the eighties. By the mid-1980s, semiconductor designs had outgrown the practical limits of gate-level simulation. Gate-level simulation delivered accuracy, but at glacial pace; silicon prototypes performed at real-speed but arrived far too late. The industry needed a new instrument, a verification engine capable of … Read more
FPGA prototyping and hardware emulation originated from two independent demands that emerged at roughly the same time, namely, the necessity to implement digital designs in reconfigurable hardware. This was conceivable given the newly introduced field programmable gate array (FPGA) device. Yet from the very beginning they were driven by different motivations. Hardware emulation emerged from … Read more