The market opportunities for machine learning hardware are becoming more succinct, with the following (rather broad) categories emerging:
Last week I attended the Linley Fall Processor Conference held in Santa Clara, CA. This blog is the first of three blogs I will be writing based on things I saw and heard at the event.
In April, Flex Logix announced its InferX X1 edge inference co-processor. At that time, Flex Logix announced that the IP would be available and that a chip,… Read More
In the old days, product architects would throw a functional block diagram “over the wall” to the design team, who would plan the physical implementation, analyze the timing of estimated critical paths, and forecast the signal switching activity on representative benchmarks. A common reply back to the architects was, “We’ve… Read More
eFPGA stands for embedded Field Programmable Grid Arrays. An eFPGA is a programmable device like an FPGA but rather than being sold as a completed chip it is licensed as a semiconductor IP block. ASIC designers can license this IP and embed it into their own chips adding the flexibility of programmability at an incremental cost.… Read More
Dr. Cheng Wang, Co-Founder and SVP Engineering at Flex Logix, presented the second talk in the ‘AI at the Edge’ session, at the just concluded Linley Spring Processor Conference, highlighting the InferX X1 Inference Co-Processor’s high throughout, low cost, and low power. He opened by pointing out that existing inference solutions… Read More
The basic principles used for neural networks have been understood for decades, what have changed to make them so successful in recent years are increased processing power, storage and training data. Layered on top of this is continued improvement in algorithms, often enabled by dramatic hardware performance improvements.… Read More
Frequent Semiwiki readers are well aware of the industry momentum behind machine learning applications. New opportunities are emerging at a rapid pace. High-level programming language semantics and compilers to capture and simulate neural network models have been developed to enhance developer productivity (link). Researchers… Read More
Machine learning applications in data centers (or “the cloud”) have pervasively changed our environment. Advances in speech recognition and natural language understanding have enabled personal assistants to augment our daily lifestyle. Image classification and object recognition techniques enrich our social media experience,… Read More
The design of electronic systems for aerospace applications shares many of the same constraints as apply to consumer products – e.g., cost (including NRE), power dissipation, size, time-to-market. Both market segments are driven to leverage the integration benefits of process scaling. … Read More
The traditional metrics for evaluating IP are performance, power, and area, commonly abbreviated as PPA. Viewed independently, PPA measures can be difficult to assess. As an example, design constraints that are purely based on performance, without concern for the associated power dissipation and circuit area, are increasingly… Read More
