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Maximizing Edge AI Performance


Inference of convolutional neural network models is algorithmically straightforward, but to get the fastest performance for your application there are a few pitfalls to keep in mind when deploying. A number of factors make efficient inference difficult, which we will first step through before diving into specific solutions to address and resolve each. By the end of this article, you will be arm... » read more

The Best AI Edge Inference Benchmark


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

Edge Inference Applications And Market Segmentation


Until recently, most AI was in data centers/cloud and most of that was training. Things are changing quickly. Projections are AI sales will grow rapidly to tens of billions of dollars by the mid 2020s, with most of the growth in edge AI inference. Data center/cloud vs. edge inference: What’s the difference? The data center/cloud is where inference started on Xeons. To gain efficiency, much ... » read more

ResNet-50 Does Not Predict Inference Throughput For MegaPixel Neural Network Models


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


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

Apples, Oranges & The Optimal AI Inference Accelerator


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

Performance Metrics For Convolutional Neural Network Accelerators


Across the industry, there are few benchmarks that customers and potential end users can employ to evaluate an inference acceleration solution end-to-end. Early on in this space, the performance of an accelerator was measured as a single number: TOPs. However, the limitations of using a single number has been covered in detail in the past by previous blogs. Nevertheless, if the method of cal... » read more

Integrating FPGA: Comparison Of Chiplets Vs. eFPGA


FPGA is widely popular in systems for its flexibility and adaptability. Increasingly, it is being used in high volume applications. As volumes grow, system designers can consider integration of the FPGA into an SoC to reduce cost, reduce power and/or improve performance. There are two options for integrating FPGA into an SoC: FPGA chiplets, which replace the power hungry SERDES/PHYs wit... » read more

eFPGA As Fast And Dense As FPGA, On Any Process Node


A challenge for eFPGA when we started Flex Logix is that there are many customers and applications, and they all seemed to want eFPGA on different foundries, different nodes and different array sizes. And everyone wanted the eFPGA to be as fast and as dense as FPGA leaders’ on the same node. Oh, and customers seem to wait to the last minute then need the eFPGA ASAP. Xilinx and Altera (Intel ... » read more

Increasing eFPGA Adoption Will Shape eFPGA Features/Benefits


eFPGA adoption is accelerating. eFPGA is now available from multiple suppliers for multiple foundries and on nodes including 180nm, 40nm, 28nm, 22nm, 16nm, 12nm and 7nm. There are double-digit chips proven in silicon by multiple customers for multiple applications. And many more in fab, in design and in planning. The three main applications are: Integration of existing FPGA chips int... » read more

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