Hyper-Parallel Calculator-Free Neural Network Accelerator for Edge AI Applications

在这项研究中，我们的目标是为边缘人工智能应用开发一个超并行的免计算器神经网络加速器。该框架通过免计算器的神经网络和超并行加速器协同加速边缘智能的应用。具体地说，在我们提出的新型超稀疏二分神经网络拓扑结构的基础上，利用尖峰范式探索了无需计算器的神经网络，从而开发了基于我们的多粒度可重构平台和所提出的拓扑结构的超并行神经网络加速器。在2022财年，一种新的具有梯度隔离记忆机制的尖峰神经网络框架在同行评议的国际会议(HPCC 2022)上发表。相应地，Dianet使用所提出的尖峰范例实现了无需计算器的NN拓扑，并在PYTHON代码中的MNIST和NMNIST基准测试上进行了评估。此外，计算单元由查找表实现，而不是传统的乘法和加法运算，相应的数字电路设计基于Verilog代码实现。然后，我们在实际硬件(PYNQ-Z1现场可编程门阵列)上对所提出的免计算器神经网络加速器进行了测试。目前，我们正在进行实验并撰写一篇论文，将于2023财年提交给一家期刊。为了探索我们加速器应用的多样性，我们还实现了一个初步的与问题相关的应用程序，该应用程序在同行评议的国际会议(HPCC 2022)上发布。

In this research, we aim to develop a hyper-parallel calculator-free neural network accelerator for edge AI applications. Our framework collaboratively accelerates edge intelligence application through the calculator-free neural network and hyper-parallel accelerator. Specifically, the calculator-free neural network is explored by the spiking paradigm on the basis of our novel ultra-sparse bisection neural network topology, and thus the hyper-parallel neural network accelerator is developed based on our multi-grained reconfigurable platform and the proposed topology. In FY2022, a novel spiking neural network framework with gradient-isolated memorizing mechanism was published at the peer-reviewed international conference (HPCC 2022). Accordingly, the calculator-free NN topology was implemented by DiaNet with the proposed spiking paradigm, which was evaluated on MNIST and NMNIST benchmark in Python code. Furthermore, the computing units are implemented by the LUT rather than the conventional multiplication and addition operations, and corresponding digital circuit designs are implemented on Verilog code. We then test the proposed calculator-free neural network accelerator on real hardware (PYNQ-Z1 FPGA). Currently, we are conducting experiments and writing a paper that will be submitted to a journal in FY2023. To explore the diversity of applications for our accelerator, we have also implemented a preliminary question-relatedness application, which was published at the peer-reviewed international conference (HPCC 2022).