Bit Prudent In-Cache Acceleration of Deep Convolutional Neural Networks

We propose Bit Prudent In-Cache Acceleration of Deep Convolutional Neural Networks - an in-SRAM architecture for accelerating Convolutional Neural Network (CNN) inference by leveraging network redundancy and massive parallelism. The network redundancy is exploited in two ways. First, we prune and fine-tune the trained network model and develop two distinct methods - coalescing and overlapping to run inferences efficiently with sparse models. Second, we propose an architecture for network models with a reduced bit width by leveraging bit-serial computation. Our proposed architecture achieves a 17.7x/3.7x speedup over server class CPU/GPU, and a 1.6x speedup compared to the relevant in-cache accelerator, with 2% area overhead each processor die, and no loss on top-1 accuracy for AlexNet. With a relaxed accuracy limit, our tunable architecture achieves higher speedups.