Emergent Chip Vastly Accelerates Deep Neural Networks
January 11th, 2016Via: The Next Platform:
Using nine different deep neural network benchmarking suites, EIE performed inference operations anywhere (depending on the benchmark) between 13X and 189X faster over regular CPU and also GPU implementations, although this is without any compression. Still, however, consider the power envelope. As the benchmarks show, the energy efficiency is better by between 3,000X on a GPU and 24,000X on CPU.
As Han describes, this is the first accelerator for spare and weight-sharing neural networks. “Operating directly on compressed networks enables the first large neural network models to fit in SRAM, which results in far better energy savings compared to accessing from external DRAM. EIE saves 65.16 percent energy by avoiding weight reference and arithmetic as the 70 percent of activations that are zero in a typical deep learning application.”
