tag: neural networks

Compiling And Optimizing Neural Nets
By Bryon Moyer - 10 Sep, 2020 - Comments: 0

Edge inference engines often run a slimmed-down real-time engine that interprets a neural-network model, invoking kernels as it goes. But higher performance can be achieved by pre-compiling the model and running it directly, with no interpretation — as long as the use case permits it. At compile time, optimizations are possible that wouldn’t be available if interpreting. By quantizing au... » read more

For AI Hardware, Power Optimization Starts With Software And Ends At Silicon
By Solaiman Rahim - 04 Sep, 2020 - Comments: 0

Artificial intelligence (AI) processing hardware has emerged as a critical piece of today’s tech innovation. AI hardware architecture is very symmetric with large arrays of up to thousands of processing elements (tiles), leading to billion+ gate designs and huge power consumption. For example, the Tesla auto-pilot software stack consumes 72W of power, while the neural network accelerator cons... » read more

Engineering Within Constraints
By Brian Bailey - 30 Jul, 2020 - Comments: 0

One of the themes of DAC this year was the next phase of machine learning. It is as if CNNs and RNNs officially have migrated from the research community and all that is left now is optimization. The academics need something new. Quite correctly, they have identified power as the biggest problem associated with learning and inferencing today, and a large part of that problem is associated with ... » read more

It’s Eternal Spring For AI
By Rama Chellappa - 14 Jul, 2020 - Comments: 0

The field of Artificial Intelligence (AI) has had many ups and downs largely due to unrealistic expectations created by everyone involved including researchers, sponsors, developers, and even consumers. The “reemergence” of AI has lot to do with recent developments in supporting technologies and fields such as sensors, computing at macro and micro scales, communication networks and progre... » read more

What Will The Next-Gen Verification Flow Look Like?
By Ed Sperling - 28 May, 2020 - Comments: 0

Semiconductor Engineering sat down to discuss what's ahead for verification with Daniel Schostak, Arm fellow and verification architect; Ty Garibay, vice president of hardware engineering at Mythic; Balachandran Rajendran, CTO at Dell EMC; Saad Godil, director of applied deep learning research at Nvidia; and Nasr Ullah, senior director of performance architecture at SiFive. What follows are exc... » read more

Spiking Neural Networks: Research Projects or Commercial Products?
By Bryon Moyer - 18 May, 2020 - Comments: 2

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

The Murky World Of AI Benchmarks
By Ann Mutschler - 30 Apr, 2020 - Comments: 0

AI startup companies have been emerging at breakneck speed for the past few years, all the while touting TOPS benchmark data. But what does it really mean and does a TOPS number apply across every application? Answer: It depends on a variety of factors. Historically, every class of design has used some kind of standard benchmark for both product development and positioning. For example, SPEC... » read more

More Multiply-Accumulate Operations Everywhere
By Ed Sperling - 02 Apr, 2020 - Comments: 1

Geoff Tate, CEO of Flex Logix, sat down with Semiconductor Engineering to talk about how to build programmable edge inferencing chips, embedded FPGAs, where the markets are developing for both, and how the picture will change over the next few years. SE: What do you have to think about when you're designing a programmable inferencing chip? Tate: With a traditional FPGA architecture you ha... » read more

Memory Issues For AI Edge Chips
By Mark LaPedus - 23 Mar, 2020 - Comments: 3

Several companies are developing or ramping up AI chips for systems on the network edge, but vendors face a variety of challenges around process nodes and memory choices that can vary greatly from one application to the next. The network edge involves a class of products ranging from cars and drones to security cameras, smart speakers and even enterprise servers. All of these applications in... » read more

Going On the Edge
By Mark LaPedus - 27 Jan, 2020 - Comments: 3

Emmanuel Sabonnadière, chief executive of Leti, sat down with Semiconductor Engineering to talk about artificial intelligence (AI), edge computing and chip technologies. What follows are excerpts of that conversation. SE: Where is AI going in the future? Sabonnadière: I am a strong believer that edge AI will change our lives. Today’s microelectronics are organized with 80% of things i... » read more

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