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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

Understanding The Performance Of Processor IP Cores


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

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

The Murky World Of AI Benchmarks


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

Evaluating NVMe SSD Multi-Gigabit Performance


The multi-channel parallelism and low-latency access of NAND flash technology have made Non-Volatile Memory express (NVMe) based SSDs very popular within the main segments of the data storage market, including not only the consumer electronics sector but also data center processing and acceleration services, where the key role is played by specialized FPGA-based hardware for application-specifi... » read more

Power/Performance Bits: Jan. 28


Accelerator-on-chip Researchers at Stanford University and SLAC National Accelerator Laboratory created an electron-accelerator-on-chip. While the technique is much less powerful than standard particle accelerators, it can be much smaller. It relied upon an infrared laser to deliver, in less than a hair’s width, the sort of energy boost that takes microwaves many feet. The team carved ... » read more

Defining And Improving AI Performance


Many companies are developing AI chips, both for training and for inference. Although getting the required functionality is important, many solutions will be judged by their performance characteristics. Performance can be measured in different ways, such as number of inferences per second or per watt. These figures are dependent on a lot of factors, not just the hardware architecture. The optim... » read more

MLPerf Benchmarks


Geoff Tate, CEO of Flex Logix, talks about the new MLPerf benchmark, what’s missing from the benchmark, and which ones are relevant to edge inferencing. » read more

Revving Up For Edge Computing


The edge is beginning to take shape as a way of limiting the amount of data that needs to be pushed up to the cloud for processing, setting the stage for a massive shift in compute architectures and a race among chipmakers for a stake in a new and highly lucrative market. So far, it's not clear which architectures will win, or how and where data will be partitioned between what needs to be p... » read more

Better Benchmarks Through Compiler Optimizations: Codasip Jump Threading


The architectural efficiency of embedded processor IP is measured by a small set of industry standard benchmarks, that even though often bear little correlation to real workloads, continue to persist. The most popular benchmarks are Dhrystone and CoreMark. An interesting observation regarding these test suites is that the performance numbers continue to improve for a given architecture, even... » read more

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