Software Stack For Edge AI Performance


Developing an agile software stack is important for successful AI deployment on the edge. We regularly encounter new machine learning models created from multiple AI frameworks that leverage the latest primitives and state-of-the-art ML model topologies. This Cambrian explosion has resulted from a fertile open-source community that has embraced AI and is now fueling a wide proliferation of ML m... » read more

Application-Optimized Processors


Executing a neural network on top of an NPU requires an understanding of application requirements, such as latency and throughput, as well as the potential partitioning challenges. Sharad Chole, chief scientist and co-founder of Expedera, talks about fine-grained dependencies, why processing packets out of order can help optimize performance and power, and when to use voltage and frequency scal... » read more

Accelerating Analog Design Migration


Today’s electronic chips are commonly comprised of a mix of analog, RF, and digital components, with increasing functionalities, complexities, and numbers of transistors reaching the trillions. While the digital side of the house can take advantage of automated design implementation tools, the analog world has always been more about doing things manually and in a very “custom” way—which... » read more

Network-on-Chips Enabling Artificial Intelligence/Machine Learning Everywhere


Recently, I attended the AI HW Summit in Santa Clara and Autosens in Brussels. Artificial intelligence and machine learning (AI/ML) were critical themes for both events, albeit from different angles. While AI/ML as a buzzword is very popular these days in all its good and bad ways, in discussions with customers and prospects, it became clear that we need to be precise in defining what type of A... » read more

IBM’s Energy-Efficient NorthPole AI Unit


At this point it is well known that from an energy efficiency standpoint, the biggest bang for the back is to be found at the highest levels of abstraction. Fitting the right architecture to the task at hand i.e., an application specific architecture, will lead to benefits that are hard or impossible to claw back later in the design and implementation flow.  With the huge increase in the inter... » read more

Thoughts On AI Consciousness


By Anda Ioana Enescu Buyruk and Catalin Tudor The rapid advancement of artificial intelligence (AI) has sparked profound discussions regarding the possibility of AI systems achieving consciousness. Such a development carries immense implications, forcing us to redirect our focus from studying the behavior of other organisms to scrutinizing ourselves. This article will delve into the concept ... » read more

Unleashing The Power Of Generative AI In Chip, System, And Product Design


The field of chip, system, and product design is a complex landscape, fraught with challenges that designers grapple with daily. The traditional design process, while robust, often falls short in addressing the increasing demands for efficiency, customization, and innovation. This white paper delves into these challenges, exploring the transformative potential of generative artificial int... » read more

When And Where To Implement AI/ML In Fabs


Deciphering complex interactions between variables is where machine learning and deep learning shine, but figuring out exactly how ML-based systems will be most useful is the job of engineers. The challenge is in pairing their domain expertise with available ML tools to maximize the value of both. This depends on sufficient quantities of good data, highly optimized algorithms, and proper tra... » read more

Quantum Plus AI Widens Cyberattack Threat Concerns


Quantum computing promises revolutionary changes to the computing paradigm that the semiconductor industry has operated under for decades, but it also raises the prospect of widespread cybersecurity threats. Quantum computing cyberattacks will occur millions of times faster than any assault conventional computing can muster. And while quantum computing is in an early stage of development, ex... » read more

Patterns And Issues In AI Chip Design


AI is becoming more than a talking point for chip and system design, taking on increasingly complex tasks that are now competitive requirements in many markets. But the inclusion of AI, along with its machine learning and deep learning subcategories, also has injected widespread confusion and uncertainty into every aspect of electronics. This is partly due to the fact that it touches so many... » read more

← Older posts Newer posts →