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Glad this helped clarify things for you! The tricky thing about benchmarks is that one of the key benefits of Tensil is the flexibility to find a trade-off betw
by tdba 5y ago
Glad this helped clarify things for you! The tricky thing about benchmarks is that one of the key benefits of Tensil is the flexibility to find a trade-off between performance, accuracy, cost and power usage that works for you. Benchmarks that only consider performance or performance per watt can be a bit narrow from that point of view. That said, this is a good idea and we'll add some comparisons that we think make sense to the docs!
- touisteur 5y agoI wanted to add something about the xilinx dpu, and you brushed on the subject but I was quite unhappy with the softip thing. It embeds all instructions for all kinds of networks so, taking a lot of gates for unused features, it's not much customizable, and perf for anything else than vanilla conv2d stuff quickly gets down. Buying an Alveo board to get such low inference perf was a gutpunch. FINN seems far better there. At least you get millions inference/sec on simple quantized CNN1Ds. The xrt api is simple and relatively ok, too. Stream data, execute inference, fetch results, mostly sync, so you have to wrap a lot of threading there, but the basics are there.
- tdba 5y agoYep, this is something we've heard before. If you're really familiar with the Xilinx ecosystem, one way we've described Tensil is that it is the "Microblaze for ML" - easy to use, lots of flexibility and customizability, with performance good enough for most applications. The DPU and FINN would then be the more specialized tool for situations where you need specific features they are optimized for.
- lagrange77 5y agoThank you!