Laconic deep learning inference acceleration
Sayeh Sharify, Alberto Delmás Lascorz, Mostafa Mahmoud, Miloš Nikolić, Kevin Siu, Dylan Malone Stuart, Zissis Poulos, Andreas Moshovos · 2019
We present a method for transparently identifying ineffectual computations during inference with Deep Learning models. Specifically, by decomposing multiplications down to the bit level, the amount of work needed by multiplications during inference can be potentially reduced by at least 40× across a wide selection of neural networks (8b and 16b). This method produces numerically identical results and does not affect overall accuracy. We present Laconic, a hardware accelerator that implements this approach to boost energy efficiency for inference with Deep Learning Networks. Laconic judiciously gives up some of the work reduction potential to yield a low-cost, simple, and energy efficient design that outperforms other state-of-the-art accelerators: an optimized DaDianNao-like design [13], Eyeriss [15], SCNN [71], Pragmatic [3], and BitFusion [83]. We study 16b, 8b, and 1b/2b fixed-point quantized models.