tag: neural networks

One More Time: TOPS Do Not Predict Inference Throughput
By Geoff Tate - 08 Oct, 2020 - Comments: 0

Many times you’ll hear vendors talking about how many TOPS their chip has and imply that more TOPS means better inference performance. If you use TOPS to pick your AI inference chip, you will likely not be happy with what you get. Recently, Vivienne Sze, a professor at MIT, gave an excellent talk entitled “How to Evaluate Efficient Deep Neural Network Approaches.” Slides are also av... » read more

Have Processor Counts Stalled?
By Brian Bailey - 24 Sep, 2020 - Comments: 2

Survey data suggests that additional microprocessor cores are not being added into SoCs, but you have to dig into the numbers to find out what is really going on. The reasons are complicated. They include everything from software programming models to market shifts and new use cases. So while the survey numbers appear to be flat, market and technology dynamics could have a big impact in resh... » read more

Neural Networks Without Matrix Math
By Katherine Derbyshire - 17 Sep, 2020 - Comments: 0

The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren't the only path forward. Almost all commercial machine learning applications depend on artificial neural networks, which are trained using large datasets with a back-propagation algorithm. The network first analyzes a training example, typically assign... » read more

AI Inference Acceleration
By Ed Sperling - 14 Sep, 2020 - Comments: 0

Geoff Tate, CEO of Flex Logix, talks about considerations in choosing an AI inference accelerator, how that fits in with other processing elements on a chip, what tradeoffs are involved with reducing latency, and what considerations are the most important. » read more

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

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