Physics-Aware AI Is The Key To Next Gen IC Design


Chip design projects are notorious for generating huge amounts of design data. The design process calls for a dozen or more electronic design automation (EDA) software tools to be run in sequence. Together, they write out hundreds of gigabytes of intermediate data on the way to creating a final layout for manufacturing. Traditionally, this has been seen as a problem. But this richness of data i... » read more

Vision Is Why LLMs Matter On The Edge


Large Language Models (LLMs) have taken the world by storm since the 2017 Transformers paper, but pushing them to the edge has proved problematic. Just this year, Google had to revise its plans to roll out Gemini Nano on all new Pixel models — the down-spec’d hardware options proved unable to host the model as part of a positive user experience. But the implementation of language-focused mo... » read more

High-Level Synthesis Propels Next-Gen AI Accelerators


Everything around you is getting smarter. Artificial intelligence is not just a data center application but will be deployed in all kinds of embedded systems that we interact with daily. We expect to talk to and gesture at them. We expect them to recognize and understand us. And we expect them to operate with just a little bit of common sense. This intelligence is making these systems not just ... » read more

Efficient Electronics


Attention nowadays has turned to the energy consumption of systems that run on electricity. At the moment, the discussion is focused on electricity consumption in data centers: if this continues to rise at its current rate, it will account for a significant proportion of global electricity consumption in the future. Yet there are other, less visible electricity consumers whose power needs are a... » read more

How To Successfully Deploy GenAI On Edge Devices


Generative AI (GenAI) burst onto the scene and into the public’s imagination with the launch of ChatGPT in late 2022. Users were amazed at the natural language processing chatbot’s ability to turn a short text prompt into coherent humanlike text including essays, language translations, and code examples. Technology companies – impressed with ChatGPT’s abilities – have started looking ... » read more

DDR5 PMICs Enable Smarter, Power-Efficient Memory Modules


Power management has received increasing focus in microelectronic systems as the need for greater power density, efficiency and precision have grown apace. One of the important ongoing trends in service of these needs has been the move to localizing power delivery. To optimize system power, it’s best to deliver as high a voltage as possible to the endpoint where the power is consumed. Then a... » read more

How Quickly Can You Take Your Idea To Chip Design?


Gone are the days of expensive tapeouts only done by commercial companies. Thanks to Tiny Tapeout, students, hobbyists, and more can design a simple ASIC or PCB design and actually send it to a foundry for a small fraction of the usual cost. Learners from all walks of life can use the resources to learn how to design a chip, without signing an NDA or installing licenses, faster than ever before... » read more

Enabling Multiscale Simulation


As product development teams face increasingly complex challenges — including the need for greater sustainability — there’s a growing awareness of the critical contributions made by materials. Many of our most pressing engineering challenges, from renewable energy grids to green transportation, rely on identifying or creating the right materials. Historically, materials discovery, mate... » read more

MPAM-Style Cache Partitioning With ATP-Engine And gem5


The Memory Partitioning and Monitoring (MPAM) Arm architecture supplement allows for memory resources (MPAM MSCs) to be partitioned using PARTID identifiers. This allows privileged software, like OSes and hypervisors to partition caches, memory controllers and interconnects on the hardware level. This allows for bandwidth and latency controls to be defined and enforced for memory requestors. ... » read more

Fallback Fails Spectacularly


Conventional AI/ML inference silicon designs employ a dedicated, hardwired matrix engine – typically called an “NPU” – paired with a legacy programmable processor – either a CPU, or DSP, or GPU. The common theory behind these two-core (or even three core) architectures is that most of the matrix-heavy machine learning workload runs on the dedicated accelerator for maximum efficienc... » read more

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