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

Variables Complicate Safety-Critical Device Verification


The inclusion of AI chips in automotive and increasingly in avionics has put a spotlight on advanced-node designs that can meet all of the ASIL-D requirements for temperature and stress. How should designers approach this task, particularly when these devices need to last longer than the applications? Semiconductor Engineering sat down to discuss these issues with Kurt Shuler, vice president of... » read more

ML Opening New Doors For FPGAs


FPGAs have long been used in the early stages of any new digital technology, given their utility for prototyping and rapid evolution. But with machine learning, FPGAs are showing benefits beyond those of more conventional solutions. This opens up a hot new market for FPGAs, which traditionally have been hard to sustain in high-volume production due to pricing, and hard to use for battery-dri... » read more

Challenges In Building Smarter Systems


Semiconductor Engineering sat down to define what the edge will look like with Jeff DeAngelis, managing director of the Industrial and Healthcare Business Unit at Maxim Integrated; Norman Chang, chief technologist at Ansys; Andrew Grant, senior director of artificial intelligence at Imagination Technologies; Thomas Ensergueix, senior director of the automotive and IoT line of business at Arm; V... » read more

Data Strategy Shifting Again In Cars


Carmakers are modifying their data processing strategies to include more processing at or near the source of data, reducing the amount of data that needs to be moved around within a vehicle to both improve response time and free up compute resources. These moves are a world away from the initial idea that terabytes of streaming data would be processed in the cloud and sent back to the vehicl... » read more

Conflicting Demands At The Edge


Semiconductor Engineering sat down to define what the edge will look like with Jeff DeAngelis, managing director of the Industrial and Healthcare Business Unit at Maxim Integrated; Norman Chang, chief technologist at Ansys; Andrew Grant, senior director of artificial intelligence at Imagination Technologies; Thomas Ensergueix, senior director of the automotive and IoT line of business at Arm; V... » read more

Choosing Between CCIX And CXL


Semiconductor Engineering sat down to the discuss the pros and cons of the Compute Express Link (CXL) and the Cache Coherent Interconnect for Accelerators (CCIX) with Kurt Shuler, vice president of marketing at Arteris IP; Richard Solomon, technical marketing manager for PCI Express controller IP at Synopsys; and Jitendra Mohan, CEO of Astera Labs. What follows are excerpts of that conversati... » 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

Which Chip Interconnect Protocol Is Better?


Semiconductor Engineering sat down to the discuss the pros and cons of the Compute Express Link (CXL) and the Cache Coherent Interconnect for Accelerators (CCIX) with Kurt Shuler, vice president of marketing at Arteris IP; Richard Solomon, technical marketing manager for PCI Express controller IP at Synopsys; and Jitendra Mohan, CEO of Astera Labs. What follows are excerpts of that conversation... » read more

Inference Moves To The Network


Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future inference capabilities will reside not only at the extremes of the data center and a data-gathering device, but at multiple points in between. “Inference isn't a function that has to resid... » read more

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