I-Corps: Harnessing Unary Computing for Modern Applications

I-Corps：利用一元计算实现现代应用

• 批准号: 2031325
• 负责人: Kia Bazargan
• 金额: $ 5万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031325&HistoricalAwards=false
• 关键词: Corps; Harnessing; Unary; Computing; Modern

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.

该I-Corps项目的更广泛的影响/商业潜力是开发一种名为“ Unary Computing”的新型计算方法，该方法具有有限的硬件资源以执行复杂的计算，使计算非常有效。此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，卫星以及云计算和云计算（IoT）系统的机器学习。拟议的方法可以显着减少云和嵌入式系统级别上新兴应用的功率和能源需求，从而使芯片制造商和云平台公司受益。此外，可能受益于降低的硬件成本和较小电池的物联网应用包括医疗实施，超低潜伏期交易以及无人驾驶飞机（UAVS）/这个I-CORPS项目基于Unary Computing的开发，该计算的开发使用了“未压缩”数据表示（非级别的数据表示），而不是传统的二元代表来使计算效率非常有效。常规方法将需要许多乘法和加法操作来计算诸如COSH（X），TANH（X）和Y = X^0.45通过多项式扩展等功能。一级计算能够直接直接执行这些计算，而无需任何乘法操作或添加。与传统的二进制计算相比，提出的方法显示出显着的性能优势。例如，当以8位分辨率实施tanh（x）时，混合单二进制方法只能在FPGA上使用24个区域（一种特定类型的集成电路）来实现该功能，而常规二进制则需要509个单位。一元方法在其他性能指标（例如速度和功率）中的表现优于二进制。该方法可导致恒定繁殖的大约20％-30％的面积改善，这在机器学习应用中大量使用。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响来审查标准，被认为是通过评估来获得珍贵的支持。