Market assessment of an Spiking Neuron Implementation in Digital Hardware using a Sampling-Based Approach for Reduced Power Consumption

AI解决方案每分钟都在生成和处理大量数据。速度和功率正成为公司考虑的越来越重要的因素，许多人一直在寻找神经形态系统作为答案。神经形态系统根据人脑和神经系统中的系统对计算机的元素进行建模。真实的神经系统的最典型的特性之一是它们与电子硬件系统相比显著低的功耗。考虑到在真实的生物系统中发现的并行神经元的大型网络，低功耗对于有效且可行的电气硬件复制至关重要。许多提出的尖峰发放神经元和神经处理器实现与它们提出之前的实现相比提供了大量的功率节省，然而在每种情况下，对实现施加了限制或约束，诸如需要使用特定技术节点、神经元模型和/或硬件实现技术。所提出的正在申请专利的新型方法，以数字硬件实现的尖峰神经元评估神经元的微分方程的频率依赖于输入电流使用基于采样的方法。采样频率的可变性减少了针对低刺激状态的不必要的切换活动。这种新颖的依赖于输入的可变采样（I-DEVS）数字实现方法用于尖峰神经元实现，导致神经元具有最小的附加硬件资源使用，以换取显著的动态功率节省，因为与传统神经元实现相比，切换活动大大减少。该提案将进行市场评估，以更好地了解如何将这项创新技术从实验室推向市场。

Vast amounts of data are being generated and processed every single minute by AI solutions. Speed and power are becoming increasingly important factors for companies to consider and many have been looking toward neuromorphic systems as the answer. Neuromorphic systems model elements of a computer after systems in the human brain and nervous system. One of the most characteristic properties of real neural systems is their remarkably low power consumption in comparison to electrical hardware systems. Given the large networks of parallel neurons found in real biological systems, low power consumption is paramount to an effective and feasible electrical hardware replication. Many proposed spiking neuron and neuro-processor implementations offer substantial power savings compared to implementations prior to their proposal, however in each case limitations or restrictions are imposed on the implementation such as the use of a specific technology node, neuron model, and/or hardware implementation technique are required. The proposed patent-pending novel approach to digital hardware implementations of spiking neurons evaluates the neuron's differential equations at a frequency dependent on input current using a sampling-based approach. The variability in the frequency of the sampling reduces unnecessary switching activity for low-stimulus states. This novel Input-DEpendent Variable Sampling (I-DEVS) digital realization method to spiking neuron implementation results in neurons with minimal additional hardware resource usage in exchange for dramatic dynamic power savings as the switching activity is greatly reduced compared to traditional neuron implementations. This proposal will be working on a market assessment to better understand how to bring this innovative technology from the lab to the market.