This session includes papers that present innovative solutions to improve efficiency and performance of neural networks. The first two papers present efficient implementations of the Winograd convolutional neural network (CNN) algorithm. The first paper proposes a sparse-optimized dataflow and a load-balancing algorithm for enhancing CNN efficiency. The second paper focuses on an efficient implementation targeting IoT edge devices. The third paper discusses a kernel transformation method to reduce computations and improve performance and power efficiency of binary- and ternary-weight neural networks. The fourth paper pursues mapping XNOR and bitcount operations in binary neural networks onto content addressable memory (CAM) arrays.