中文 English

Foundational Changes In Chip Architectures


We take many things in the semiconductor world for granted, but what if some of the decisions made decades ago are no longer viable or optimal? We saw a small example with finFETs, where the planar transistor would no longer scale. Today we are facing several bigger disruptions that will have much larger ripple effects. Technology often progresses in a linear fashion. Each step provides incr... » read more

Rethinking Machine Learning For Power


The power consumed by machine learning is exploding, and while advances are being made in reducing the power consumed by them, model sizes and training sets are increasing even faster. Even with the introduction of fabrication technology advances, specialized architectures, and the application of optimization techniques, the trend is disturbing. Couple that with the explosion in edge devices... » read more

AI Power Consumption Exploding


Machine learning is on track to consume all the energy being supplied, a model that is costly, inefficient, and unsustainable. To a large extent, this is because the field is new, exciting, and rapidly growing. It is being designed to break new ground in terms of accuracy or capability. Today, that means bigger models and larger training sets, which require exponential increases in processin... » read more

Can Analog Make A Comeback?


We live in an analog world dominated by digital processing, but that could change. Domain specificity, and the desire for greater levels of optimization, may provide analog compute with some significant advantages — and the possibility of a comeback. For the last four decades, the advantages of digital scaling and flexibility have pushed the dividing line between analog and digital closer ... » read more

Data Center Architectures In Flux


Data center architectures are becoming increasingly customized and heterogeneous, shifting from processors made by a single vendor to a mix of processors and accelerators made by multiple vendors — including system companies' own design teams. Hyperscaler data centers have been migrating toward increasingly heterogeneous architectures for the past half decade or so, spurred by the rising c... » read more

Week In Review: Auto, Security, Pervasive Computing


Automotive Infineon announced a new MEMS scanner chipset for automotive heads-up displays (HUD) and AR (augmented reality) eyeglasses. The design has MEMS mirror — which tilts and can work with laser beam scanner (LBS) projectors — and MEMS driver. The size and energy use is small and yet it projects content over a wider area of the windshield. A partnership between Ansys and IPG Automo... » read more

Architectural Considerations For AI


Custom chips, labeled as artificial intelligence (AI) or machine learning (ML), are appearing on a weekly basis, each claiming to be 10X faster than existing devices or consume 1/10 the power. Whether that is enough to dethrone existing architectures, such as GPUs and FPGAs, or whether they will survive alongside those architectures isn't clear yet. The problem, or the opportunity, is that t... » read more

11 Ways To Reduce AI Energy Consumption


As the machine-learning industry evolves, the focus has expanded from merely solving the problem to solving the problem better. “Better” often has meant accuracy or speed, but as data-center energy budgets explode and machine learning moves to the edge, energy consumption has taken its place alongside accuracy and speed as a critical issue. There are a number of approaches to neural netw... » read more

Putting Limits On What AI Systems Can Do


New techniques and approaches are starting to be applied to AI and machine learning to ensure they function within acceptable parameters, only doing what they're supposed to do. Getting AI/ML/DL systems to work has been one of the biggest leaps in technology in recent years, but understanding how to control and optimize them as they adapt isn't nearly as far along. These systems are generall... » read more

Mythic Case Study


Mythic, the provider of a unique AI compute platform, was designing an innovative intelligence processing unit (IPU) and found themselves in need of a small, power-efficient, yet programmable core to take care of specific supporting functions. As no off-the-shelf core would exactly match the needs and customization proved challenging, Mythic eventually opted for a complete solution by Codasip. ... » read more

← Older posts