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The astonishing development in the field of artificial neural networks (ANN) has brought significant advancement in many application domains, such as pattern recognition, image classification, and computer vision. ANN imitates neuron behaviors and makes a decision or prediction by learning patterns and features from the given data set. To reach higher accuracies, neural networks are getting deeper, and consequently, the computation and storage demands on hardware platforms are steadily increasing. In addition, the massive data communication among neurons makes the interconnection more complex and challenging. To overcome these challenges, ASIC-based DNN accelerators are being designed which usually incorporate customized processing elements, fixed interconnection, and large off-chip memory storage. As a result, DNN computation involves large memory accesses due to frequent load/off-loading data, which significantly increases the energy consumption and latency. Also, the rigid architecture and interconnection among processing elements limit the efficiency of the platform to specific applications. In recent years, Network-on-Chip-based (NoC-based) DNN becomes an emerging design paradigm because the NoC interconnection can help to reduce the off-chip memory accesses while offers better scalability and flexibility. To evaluate the NoC-based DNN in the early design stage, we introduce a cycle-accurate NoC-based DNN simulator, called DNNoC-sim. To support various operations such as convolution and pooling in the modern DNN models, we first propose a DNN flattening technique to convert diverse DNN operation into MAC-like operations. In addition, we propose a DNN slicing method to evaluate the large-scale DNN models on a resource-constraint NoC platform. The evaluation results show a significant reduction in the off-chip memory accesses compared to the state-of-the-art DNN model. We also analyze the performance and discuss the trade-off between different design parameters., QC 20201201

Deep Neural Networks (DNN) have shown significant advantagesin many domains such as pattern recognition, prediction, and controloptimization. The edge computing demand in the Internet-of-Things era has motivated many kinds of computing platforms toaccelerate the DNN operations. The most common platforms areCPU, GPU, ASIC, and FPGA. However, these platforms suffer fromlow performance (i.e., CPU and GPU), large power consumption(i.e., CPU, GPU, ASIC, and FPGA), or low computational flexibilityat runtime (i.e., FPGA and ASIC). In this paper, we suggest theNoC-based DNN platform as a new accelerator design paradigm.The NoC-based designs can reduce the off-chip memory accessesthrough a flexible interconnect that facilitates data exchange betweenprocessing elements on the chip. We first comprehensivelyinvestigate conventional platforms and methodologies used in DNNcomputing. Then we study and analyze different design parametersto implement the NoC-based DNN accelerator. The presentedaccelerator is based on mesh topology, neuron clustering, randommapping, and XY-routing. The experimental results on LeNet, MobileNet,and VGG-16 models show the benefits of the NoC-basedDNN accelerator in reducing off-chip memory accesses and improvingruntime computational flexibility., QC 20200604