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Recently, Deep Spiking Neural Network (DSNN) has emerged as a promising neuromorphic approach for various AI-based applications, such as image classification, speech recognition, robotic control etc. on edge computing platforms. However, the state-of-the-art offline training algorithms for DSNNs are facing two major challenges. Firstly, many timesteps are required to reach comparable accuracy with traditional frame-based DNNs algorithms. Secondly, extensive memory requirements for weight storage make it impossible to store all the weights on-chip for DSNNs with many layers. Thus the inference process requires continue access to expensive off-chip memory, ultimately leading to performance degradation in terms of throughput and power consumption. In this work, we propose a hardware-friendly training approach for DSNN that allows the weights to be constrained to ternary format, hence reducing the memory footprints and the energy consumption. Software simulations on MNIST and CIFAR10 datasets have shown that our training approach could reach an accuracy of 97% for MNIST (3-layer fully connected networks) and 89.71% for CIFAR10 (VGG16). To demonstrate the energy efficiency of our approach, we have proposed a neural processing module to implement our trained DSNN. When implemented as a fixed, 3-layers fully-connected system, the system has reached at energy efficiency of 74nJ/image with a classification accuracy of 97% for MNIST dataset. We have also considered a scalable design to support more complex network topologies when we integrate the neural processing module with a 3D Network-on-Chip. [ABSTRACT FROM AUTHOR]

While speeding up cryptography tasks can be accomplished by using a multi-core architecture to parallelize computation, one of the major challenges is optimizing power consumption. In principle, depending on the computation workload, individual cores can be turned off to save power during operation. However, too few active cores may lead to computational bottlenecks. In this work, we propose a novel platform named Spike-MCryptCores: a low-power multi-core AES platform with a neuromorphic controller. The proposed Spike-MCryptCores platform is composed of multiple AES cores, each core is equipped with a clock-gating scheme for reducing its power consumption while being idle. To optimize the power consumption of the whole platform, we use a neuromorphic controller. Therefore, a comprehensive framework to generate a data set, train the neural network, and produce hardware configuration for the Spiking Neural Network (SNN), a brain-inspired computing paradigm, is also presented in this paper. Moreover, Spike-MCryptCores integrates the hardware SNN inside its architecture to support low-cost and low-latency adaptations. The results show that implemented SNN controller occupies only 2.3 % of the overall area cost while providing the ability to reduce power consumption significantly. The lightweight SNN controller model is trained and tested with up to 95 % accuracy. The maximum difference between the predicted number of cores and the ideal one from the label is one unit only. Under 24 test scenarios, a SNN controller with clock-gating helps Spike-MCryptCores reducing the power consumption by 48.6 % on the average; by 67 % for the best-case scenario, and by 39 % for the worst-case scenario. [ABSTRACT FROM AUTHOR]