The field of neuromorphic computing is rapidly evolving. As both biological accuracy and practical implementations are explored, existing architectures are modified and improved for both purposes. The Temporal Neural Network(TNN) style of architecture is a good basis for approximating biological neurons due to its use of timed pulses to encode data and a voltage-threshold-like system. Using the Temporal Neural Network cortical column C3S architecture design as a basis, this project seeks to augment the network's design. This project takes note of two ideas and presents their designs with the goal of improving existing cortical column architecture. One need in this field is for an encoder that could convert between common digital formats and timed neuronal spikes, as biologically accurate networks are temporal in nature. To this end, this project presents an encoder to translate between binary encoded values and timed spikes to be processed by the neural network. Another need is for the reduction of wasted processing time to idleness, caused by lengthy Gamma cycle processing bursts. To this end, this project presents a relaxation of Gamma cycles to allow for them to end arbitrarily early once the network has determined an output response. With the goal of contributing to the betterment of the field of neuromorphic computer architecture, designs for both a binary-to-spike encoder, as well as a Gamma cycle controller, are presented and evaluated for optimal design parameters, with overall system gain and performance.

翻译：神经形态计算领域正在快速发展。随着生物准确性与实际应用场景的探索，现有架构针对这两类目标进行了修改与改进。时序神经网络（TNN）架构因其采用定时脉冲编码数据以及类电压阈值系统，成为逼近生物神经元的良好基础。本项目以时序神经网络皮层柱C3S架构设计为基础，旨在增强该网络的设计方案。项目关注两个关键方向，并针对性地提出了旨在改进现有皮层柱架构的设计方案。该领域的一个需求是设计一种编码器，能够将通用数字格式与定时神经元脉冲进行相互转换，因为生物准确型网络本质上是时域性的。为此，本项目提出了一种编码器，用于将二进制编码值转换为神经网络可处理的定时脉冲。另一个需求在于减少由长伽马周期处理突发导致的空闲等待时间浪费。为此，本项目提出了一种伽马周期松弛机制，允许网络在确定输出响应后任意提前终止伽马周期。为促进神经形态计算机架构领域的发展，本文提出了二进制-脉冲编码器与伽马周期控制器两种设计方案，并基于整体系统增益与性能对其最优设计参数进行了评估。