Bigger is not Always Better: Exploiting Parallelism in Neural Network Hardware
This blog post was originally published at AImotive’s website. It is reprinted here with the permission of AImotive. When considering hardware platforms for executing high performance NNs (Neural Networks), automotive system designers frequently determine the total compute power by simply adding up each NN’s requirements. However, the approach usually leads to demands for a single […]
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