SBIR Phase I: Ultra Power-efficient Biologically-Inspired Integrated Circuit Architectures for the Processing and Classification of Analog Sensor Signals

该小型企业创新研究(SBIR)第一阶段项目展示了环境感知应用(包含多个传感器的嵌入式系统，可在噪声条件下自动推断其当前状态)的神经分类器方法的颠覆性能效优势。该价值主张有利于广泛的消费(可穿戴计算)、电信(移动手持设备)、工业(物联网边缘节点)、医疗(eHealth、便携式医疗设备、植入式设备)和军用(自主控制无人机)应用，这些应用集成了多个传感器，并考虑到电池寿命/小型机/产品重量/设备精度之间的权衡。我们解决了数字信号处理的功耗问题，这要求工程师设计电池供电的系统，以进行不受欢迎的产品权衡。我们专有的混合信号方法以独特的方式缩小了这一传统的功率/性能差距，解决了电池供电/自动供电设备的长期挑战。该项目的更广泛的影响/商业潜力包括颠覆电池供电的智能设备的传统设计方法，加速科学研究，更高效的混合信号电子课程，改善人类状况和延长健康寿命的新型便携式设备，以及创造就业机会。该项目将展示基本突破，这些突破随后可以扩展到创建新一代低功耗可配置计算设备，其潜在影响与早期突破性数字技术(现场可编程门阵列、图形处理器、嵌入式处理器)的商业化相匹配。在过去的30年里，手持电子产品的能效提高了1000倍；我们的方法使潜在应用的能效提高了1000倍。随着工具链的进步，来自不同科学学科(神经科学)的实验硬件结合了FPAA技术，将实现以前不切实际的算法，获得新的科学见解。最终的工具增强支持工程和计算机科学实验室传感器相关课程的机会。最初的可穿戴计算目标市场基于强大的技术市场契合度、加快的产品采用周期以及3亿美元的市场机会。采用FPAA技术的可穿戴设备将更容易、更安全地使用，不那么引人注目，交流也更准确。

This Small Business Innovation Research (SBIR) Phase I project demonstrates the disruptive energy-efficiency advantages of neural classifier approaches for context aware applications (embedded systems incorporating a multiplicity of sensors to autonomously infer their current state in noisy conditions). The value proposition benefits a wide range of consumer (wearable computing), telecommunication (mobile handset), industrial (internet-of-things edge nodes), medical (eHealth, portable medical devices, implanted devices), and military (autonomous control drones) applications which incorporate multiple sensors and where battery life/small form factor/product weight/device accuracy trade-offs are concerns. We address the digital signal processing power consumption problem, which requires engineers designing battery-powered systems to make undesirable product trade-offs. Our proprietary mixed signal approaches uniquely close this traditional power/performance gap, solving a long-standing challenge for battery-powered/autonomously powered devices. The broader impact/commercial potential of this project includes disruption of traditional design approaches for battery-powered intelligent devices, accelerated scientific research, more efficient mixed signal electronic curriculum, new classes of portable devices that enhance the human condition and extend healthy life, and job creation. This project will demonstrate fundamental breakthroughs which can subsequently be extended to create a new generation of low-power configurable computing devices with potential impact matching the commercialization of earlier break-through digital technologies (FPGA, GPU, embedded processor). Over the past 30 years, handheld electronics have improved energy efficiency 1000X; our approach enables another factor of 1000 improvement in energy efficiency for potential applications. With advances in the tool-chain, experimental hardware from diverse scientific disciplines (neuroscience) incorporating FPAA technology will implement formerly impractical algorithms, achieving fresh scientific insights. Eventual tool enhancements support opportunities in engineering and computer science laboratory sensor-related curriculum. The initial wearable computing target market was based on strong technology-market fit, accelerated product adoption cycles, and a $300M market opportunity. Wearable devices incorporating FPAA technology will be easier and safer to use, less obtrusive, and communicate more accurately.