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

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

Layout Generators For Artificial Intelligence Hardware Design


Artificial intelligence (AI) is a powerful tool that offers great convenience in many areas of life. In addition to improving Internet searches and online shopping, it enables driver assistance systems that can save lives, for example. AI in its various forms is the essential tool for such applications, and it can be expected to show a similar development as microelectronics did. Although AI... » read more

AI Requires Tailored DRAM Solutions


For over 30 years, DRAM has continuously adapted to the needs of each new wave of hardware spanning PCs, game consoles, mobile phones and cloud servers. Each generation of hardware required DRAM to hit new benchmarks in bandwidth, latency, power or capacity. Looking ahead, the 2020s will be the decade of artificial intelligence/machine learning (AI/ML) touching every industry and applicatio... » read more

CodaCache: Helping to Break the Memory Wall


As artificial intelligence (AI) and autonomous vehicle systems have grown in complexity, system performance needs have begun to conflict with latency and power consumption requirements. This dilemma is forcing semiconductor engineers to re-architect their system-on-chip (SoC) designs to provide more scalable levels of performance, flexibility, efficiency, and integration. From the edge to data ... » read more

AI Roadmap: A human-centric approach to AI in aviation


Source: EASA European Union Aviation Safety Agency February 2020 "EASA published its Artificial Intelligence Roadmap 1.0 which establishes the Agency’s initial vision on the safety and ethical dimensions of development of AI in the aviation domain. The AI Roadmap 1.0 is to be viewed as a starting point, intended to serve as a basis for discussion with the Agency’s stakeholders. It... » read more

Bigger, Faster, More Diverse And Expensive


Aart de Geus, chairman and co-CEO of Synopsys, sat down with Semiconductor Engineering to talk about the race toward AI everywhere, how splintering markets are affecting design, and why software is now such a critical component of hardware development. SE: We're seeing big advances in compute performance due to advanced packaging and heterogeneous architectures. Is that sustainable? de Ge... » read more

Defining And Improving AI Performance


Many companies are developing AI chips, both for training and for inference. Although getting the required functionality is important, many solutions will be judged by their performance characteristics. Performance can be measured in different ways, such as number of inferences per second or per watt. These figures are dependent on a lot of factors, not just the hardware architecture. The optim... » read more

Speeding Up 3D Design


2.5D and 3D designs have garnered a lot of attention recently, but when should these solutions be considered and what are the dangers associated with them? Each new packaging option trades off one set of constraints and problems for a different set, and in some cases the gains may not be worth it. For other applications, they have no choice. The tooling in place today makes it possible to de... » read more

Multiphysics Simulations for AI Silicon to System Success


Achieving power efficiency, power integrity, signal integrity, thermal integrity and reliability is paramount for enabling product success by overcoming the challenges of size and complexity in AI hardware and optimizing the same for rapidly evolving AI software. ANSYS’ comprehensive chip, package and system solutions empower AI hardware designers by breaking down design margins and siloed de... » read more

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