Wrenching Efficiency Out of Custom Deep Learning Accelerators

Custom accelerators for neural network training have garnered plenty of attention in the last couple of years, but without significant software footwork, many are still difficult to program and could leave efficiencies on the table. This can be addressed through various model optimizations, but as some argue, the efficiency and utilization gaps can also be addressed with a tailored compiler. Eugenio Culurciello, an electrical engineer at Purdue University, argues that getting full computational efficiency out of custom deep learning accelerators is difficult. This prompted his team at Purdue to build an FPGA based accelerator that could be agnostic to CNN workloads and could eek maximum utilization and efficiencies on a range of deep learning tasks, including ResNet and AlexNet. Snowflake is a scalable and programmable, low-power accelerator for deep learning with a RISC based custom instruction set.