Disaggregating And Extending Operating Systems


The push toward disaggregation and customization in hardware is starting to be mirrored on the software side, where operating systems are becoming smaller and more targeted, supplemented with additional software that can be optimized for different functions. There are two main causes for this shift. The first is rising demand for highly optimized and increasingly heterogeneous designs, which... » read more

Will Floating Point 8 Solve AI/ML Overhead?


While the media buzzes about the Turing Test-busting results of ChatGPT, engineers are focused on the hardware challenges of running large language models and other deep learning networks. High on the ML punch list is how to run models more efficiently using less power, especially in critical applications like self-driving vehicles where latency becomes a matter of life or death. AI already ... » 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

Week In Review: Manufacturing, Test


Regional Shifts Supply chains are moving away from China. Apple, Honda, and Mazda are in line to diversify their manufacturing across different regions, according to one report. Another report says Apple plans to manufacture some of its new iPhone 14s in India. Mexico wants to be part of U.S.’s drive to move chip manufacturing closer to home, hosting American financiers to discuss elect... » 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

The Challenge Of Optimizing Chip Architectures For Workloads


It isn't possible to optimize a workload running on a system just by looking at hardware or software separately. They need to be developed together and intricately intertwined, an engineering feat that also requires bridging two worlds with have a long history of operating independently. In the early days of computing, hardware and software were designed and built by completely separate team... » read more

Is Programmable Overhead Worth The Cost?


Programmability has fueled the growth of most semiconductor products, but how much does it actually cost? And is that cost worth it? The answer is more complicated than a simple efficiency formula. It can vary by application, by maturity of technology in a particular market, and in the context of much larger systems. What's considered important for one design may be very different for anothe... » read more

Startup Funding: July 2021


The trend of big funding for Chinese autonomous driving companies continued in July, with three startups each drawing $100M or more for efforts in ADAS and computer vision for automotive. The month also saw one electric vehicle manufacturer get a massive boost as it begins production on its first models, while significant funding also went to a company that wants to recycle used up EV batteries... » 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

Domain-Specific Memory


Domain-specific computing may be all the rage, but it is avoiding the real problem. The bigger concern is the memories that throttle processor performance, consume more power, and take up the most chip area. Memories need to break free from the rigid structures preferred by existing software. When algorithms and memory are designed together, improvements in performance are significant and pr... » read more

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