5. ENVISION: Energy-Scalable Sparse Convolutional Neural Network Processing

This chapter focuses on Envision: two generations of energy-scalable sparse convolutional neural network processors. They achieve SotA energy-efficiency through leveraging the three key CNN-characteristics discussed in Chap. 3. (a) Inherent CNN parallelism is exploited through a highly parallelized processor architecture that minimizes internal memory bandwidth. (b) Inherent network sparsity in pruned networks and RELU activated feature maps is leveraged by compressing sparse IO-datastreams and skipping unnecessary computations. (c) The inherent fault-tolerance of CNNs is exploited by making this architecture DVAS or DVAFS compatible in Envision V1 and V2, respectively, according to the theory discussed in Chap. 4. This capability allows minimizing energy consumption for any CNN, with any computational precision requirement up to 16b fixed-point. Through its energy-scalability and high energy-efficiency, Envision lends itself perfectly for hierarchical applications, discussed in Chap. 2. It hereby enables CNN processing in always-on embedded applications.