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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

Developers Turn To Analog For Neural Nets


Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

Blog Review: May 5


Arm's William Wang considers how to increase the performance and programmability of persistent applications through using battery to protect the on-chip volatile cache hierarchy. Cadence's Paul McLellan finds that ransomware is getting more sophisticated, and more difficult to eradicate and defend against, with potentially life-threatening consequences. Synopsys' Jonathan Knudsen digs int... » read more

Edge-Inference Architectures Proliferate


First part of two parts. The second part will dive into basic architectural characteristics. The last year has seen a vast array of announcements of new machine-learning (ML) architectures for edge inference. Unburdened by the need to support training, but tasked with low latency, the devices exhibit extremely varied approaches to ML inference. “Architecture is changing both in the comp... » read more

Week In Review: Auto, Security, Pervasive Computing


Security The United States Department of Defense added China's SMIC to its blacklist for its alleged cooperation with the Chinese military, reports Reuters. U.S. investors are asked not to invest in SMIC, among 35 other companies based in China on the list. Intel Labs launched the Private AI Collaborative Research Institute with Avast and Borsetta, to advance and develop technologies in pri... » read more

Are Better Machine Training Approaches Ahead?


We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

Spiking Neural Networks: Research Projects or Commercial Products?


Spiking neural networks (SNNs) often are touted as a way to get close to the power efficiency of the brain, but there is widespread confusion about what exactly that means. In fact, there is disagreement about how the brain actually works. Some SNN implementations are less brain-like than others. Depending on whom you talk to, SNNs are either a long way away or close to commercialization. Th... » read more

Week In Review: Auto, Security, Pervasive Computing


COVID-19, IoT Last week, the United States’ Department of Health and Human Service (HHS) announced it will not enforce penalties for certain U.S. HIPAA Rules violations involving COVID-19 testing sites. HIPAA, the Health Insurance Portability and Accountability Act of 1996, protects privacy of health information. Lawyers are looking it over. "Even during the COVID-19 pandemic, providers are ... » read more

Memory Issues For AI Edge Chips


Several companies are developing or ramping up AI chips for systems on the network edge, but vendors face a variety of challenges around process nodes and memory choices that can vary greatly from one application to the next. The network edge involves a class of products ranging from cars and drones to security cameras, smart speakers and even enterprise servers. All of these applications in... » read more

The Challenges Of Building Inferencing Chips


Putting a trained algorithm to work in the field is creating a frenzy of activity across the chip world, spurring designs that range from purpose-built specialty processors and accelerators to more generalized extensions of existing and silicon-proven technologies. What's clear so far is that no single chip architecture has been deemed the go-to solution for inferencing. Machine learning is ... » read more

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