tag: benchmarks

The Best AI Edge Inference Benchmark
By Dana McCarty - 04 Mar, 2021 - Comments: 0

When evaluating the performance of an AI accelerator, there’s a range of methodologies available to you. In this article, we’ll discuss some of the different ways to structure your benchmark research before moving forward with an evaluation that directly runs your own model. Just like when buying a car, research will only get you so far before you need to get behind the wheel and give your ... » read more

The Problem With Benchmarks
By Brian Bailey - 11 Feb, 2021 - Comments: 1

Benchmarks long have been used to compare products, but what makes a good benchmark and who should be trusted with their creation? The answer to those questions is more difficult than it may appear on the surface, and some benchmarks are being used in surprising ways. Everyone loves a simple, clear benchmark, but that is only possible when the selection criteria are equally simple. Unfortuna... » read more

Edge-Inference Architectures Proliferate
By Bryon Moyer - 04 Feb, 2021 - Comments: 0

First part of two parts. The second part will dive into basic architectural characteristics. The last year has seen a vast array of announcements of new machine-learning (ML) architectures for edge inference. Unburdened by the need to support training, but tasked with low latency, the devices exhibit extremely varied approaches to ML inference. “Architecture is changing both in the comp... » read more

Standard Benchmarks For AI Innovation
By Dylan Zika - 10 Dec, 2020 - Comments: 0

There is no standard measurement for machine learning performance today, meaning there is no single answer for how companies build a processor for ML across all use cases while balancing compute and memory constraints. For the longest time, every group would pick a definition and test to suit their own needs. This lack of common understanding of performance hinders customers' buying decis... » read more

ResNet-50 Does Not Predict Inference Throughput For MegaPixel Neural Network Models
By Geoff Tate - 05 Nov, 2020 - Comments: 1

Customers are considering applications for AI inference and want to evaluate multiple inference accelerators. As we discussed last month, TOPS do NOT correlate with inference throughput and you should use real neural network models to benchmark accelerators. So is ResNet-50 a good benchmark for evaluating relative performance of inference accelerators? If your application is going to p... » read more

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

Optimizing What Exactly?
By Brian Bailey - 24 Sep, 2020 - Comments: 0

You can't optimize something without understanding it. While we inherently understand what this means, we are often too busy implementing something to stop and think about it. Some people may not even be sure what it is that they should be optimizing and that makes it very difficult to know if you have been successful. This was a key message delivered by Professor David Patterson at the Embedde... » 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

Apples, Oranges & The Optimal AI Inference Accelerator
By Geoff Tate - 03 Sep, 2020 - Comments: 0

There are a wide range of AI inference accelerators available and a wide range of applications for them. No AI inference accelerator will be optimal for every application. For example, a data center class accelerator almost certainly will be too big, burn too much power, and cost too much for most edge applications. And an accelerator optimal for key word recognition won’t have the capabil... » read more

Understanding The Performance Of Processor IP Cores
By Roddy Urquhart - 27 Aug, 2020 - Comments: 1

Looking at any processor IP, you will find that their vendors emphasize PPA (performance, power & area) numbers. In theory, they should provide a level playing field for comparing different processor IP cores, but in reality, the situation is more complex. Let us consider performance. The first thing to think about is what aspect of performance you care about. Do you care more about the ... » read more

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