I-Corps: Harnessing Unary Computing for Modern Applications

I-Corps项目更广泛的影响/商业潜力是开发一种称为“一元计算”的新型计算方法，该方法可以在有限的硬件资源下非常高效地执行复杂的计算。该方法的应用包括信号处理（图像/视频/音频/雷达）和自动驾驶汽车、无人机、卫星以及云计算和物联网（IoT）系统的机器学习。所提出的方法有可能显著降低云上和嵌入式系统级新兴应用的功耗和能源需求，从而使芯片制造商和云平台公司受益。此外，物联网应用可能受益于降低硬件成本和更小的电池，包括医疗植入物、超低延迟交易和无人驾驶飞行器(uav)/这个I-Corps项目是基于一元计算的发展，它使用“未压缩”的数据表示（一元）而不是传统的二进制表示，使计算非常高效。传统的方法需要许多乘法和加法运算来计算函数，如cosh(x), tanh(x)和y=x^0.45，通过多项式展开。一元计算能够廉价而直接地执行这些计算，而不需要任何乘法操作或加法。与传统的二进制计算相比，该方法具有显著的性能优势。例如，当以8位分辨率实现tanh(x)时，混合一元二进制方法可以在fpga（一种特定类型的集成电路）上仅使用24个单位的面积来实现该功能，而传统二进制则需要509个单位。一元方法在速度和功率等其他性能指标上优于二元方法。该方法将常系数乘法的面积提高了约20%-30%，常系数乘法在机器学习应用中被大量使用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a novel computing method called “unary computing,” which makes computations very efficient with limited hardware resources to perform complex calculations. Applications of this method include signal processing (image/video/audio/radar) and machine learning for autonomous vehicles, UAVs, satellites, as well as cloud computing and internet-of-things (IoT) systems. The proposed methodology has the potential to significantly reduce power and energy requirements for emerging applications on the cloud and at the embedded-systems level, benefiting chip manufacturers and cloud platform companies. Furthermore, IoT applications that may benefit from reduced hardware cost and smaller batteries include medical implants, ultra-low latency trading, and unmanned aerial vehicles (UAVs)/ This I-Corps project is based on the development of unary computing, which uses an “uncompressed” data representation (unary) instead of the traditional binary representation to make computations very efficient. Conventional methods would require many multiplications and addition operations to calculate functions such as cosh(x), tanh(x) and y=x^0.45 through polynomial expansions. Unary computing is able to perform these calculations cheaply and directly without any multiplication operations or additions. The proposed method shows remarkable performance benefits compared to conventional binary computations. For example, when implementing tanh(x) at 8 bits of resolution, a hybrid unary-binary method can implement the function with only 24 units of area on FPGAs (a particular type of integrated circuits), whereas conventional binary would need 509 units. The unary method outperforms binary in other performance metrics, such as speed and power. The method results in approximately 20%-30% area improvement for constant-coefficient multiplication, which is heavily used in machine learning applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.