Artificial intelligence (AI) is gaining success and importance in many different tasks. The growing pervasiveness and complexity of AI systems push researchers towards developing dedicated hardware accelerators. Spiking Neural Networks (SNN) represent a promising solution in this sense since they implement models that are more suitable for a reliable hardware design. Moreover, from a neuroscience perspective, they better emulate a human brain. This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task, using the MNIST as the target dataset. Many techniques are used to minimize the area and to maximize the performance, such as the replacement of the multiplication operation with simple bit shifts and the minimization of the time spent on inactive spikes, useless for the update of neurons' internal state. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources and reducing the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
翻译:人工智能(AI)在很多不同的任务中越来越成功和重要。 人工智能系统日益普及和复杂,促使研究人员开发专用硬件加速器。 Spiking神经网络(SNN)代表着一种很有希望的解决方案,因为它们执行的模型更适合可靠的硬件设计。 此外,从神经科学的角度来看,它们更能模仿人类大脑。 这项工作为SNN开发一个硬件加速器,进行离线培训,用于图像识别任务,使用MNIST作为目标数据集。 许多技术被用于将区域最小化和最大化性能,例如以简单的小移动替换取代倍增操作,并尽可能减少在非活动钉顶上花费的时间,对神经内部状态的更新毫无用处。 设计目标是Xilinx 艺术-7 FPGA, 总共使用约40%的现有硬件资源,将分类时间减少3级,如果与其软件完全精确对应,则对准确性影响较小4.5%。