PFI-TT: Harnessing the power of uncompressed number representation for modern computations

PFI-TT：利用未压缩数字表示的力量进行现代计算

• 批准号: 2016390
• 负责人: Kia Bazargan
• 金额: $ 25万
• 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: 2020-08-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016390&HistoricalAwards=false
• 关键词: PFI; TT; Harnessing; power; uncompressed

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to harness “Unary computing” to significantly improve the performance of select modern applications. Cloud-based machine learning, high-frequency trading, and video / audio / signal processing used in security cameras, unmanned aerial vehicles (UAVs), and video streaming are examples of such applications. The proposed methodology improves application speed while lowering hardware cost (chip size) and power for moderate resolution computations (8-12 binary digits). Cooling costs are also reduced as a direct result of reducing the power consumption of the circuits. Speed and cost are important computer processing metrics that have real impacts on industry costs. Many modern computer applications can readily benefit from these improvements using a “plug-and-play” scheme in which traditional implementations of functions are replaced with their unary counterparts without changing the rest of the system. Cloud-based machine learning, high-frequency trading, and video / audio / signal processing used in security cameras, unmanned aerial vehicles (UAVs), and video streaming are examples of such applications. The proposed project develops design methodologies harnessing unary computing, which is a new, unconventional paradigm in digital computing. An “uncompressed” data representation (unary) is used instead of the traditional binary representation of numbers to make computations very efficient. Limited hardware resources can perform complex calculations. Conventional methods of computer code often require many multiplications and addition operations to calculate functions such as cosh(x), tanh(x) and y=x0.45 through polynomial expansions. Unary computing is able to perform these calculations cheaply and directly without costly multiplications. These functions are widely used in applications in signal processing (image / video / audio / radar). Furthermore, unary computing can be used in significantly reducing the computation complexity of constant coefficient multiplication, which is widely used in machine learning (ML) applications. As validated by experiments, reducing computation complexity increases computation speed, reduces hardware area (size and cost of the chips to perform computations), and reduces power. The goal of this project is to develop a design toolset for automatically generating circuits for unary computing. Furthermore, a library of highly optimized hardware intellectual property (IP) blocks will be developed. The objectives of this research include developing the necessary toolset and design flows to help designers integrate the proposed technology in their electronic designs.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.

这一创新-技术转化伙伴关系项目的更广泛影响/商业潜力是利用"一元计算"来显著提高某些现代应用程序的性能。基于云的机器学习、高频交易以及安全摄像头、无人机（UAV）和视频流中使用的视频/音频/信号处理都是此类应用的示例。所提出的方法提高了应用程序的速度，同时降低了硬件成本（芯片大小）和中等分辨率的计算（8 - 12二进制数字）的权力。作为降低电路功耗的直接结果，冷却成本也降低了。速度和成本是重要的计算机处理指标，对行业成本有真实的影响。许多现代计算机应用程序可以使用“即插即用”方案很容易地从这些改进中受益，其中传统的函数实现被替换为一元对应函数，而无需更改系统的其余部分。基于云的机器学习、高频交易以及安全摄像头、无人机（UAV）和视频流中使用的视频/音频/信号处理都是此类应用的示例。拟议的项目开发设计方法利用一元计算，这是一个新的，非传统的范式在数字计算。使用“未压缩”的数据表示（一元）代替传统的二进制数字表示，使计算非常有效。 有限的硬件资源可以执行复杂的计算。计算机代码的常规方法通常需要许多乘法和加法运算来通过多项式展开计算诸如cosh（x）、tanh（x）和y = x0.45的函数。一元计算能够廉价地直接执行这些计算，而无需昂贵的乘法。这些功能广泛应用于信号处理（图像/视频/音频/雷达）中。此外，一元计算可以用于显着降低常系数乘法的计算复杂度，常系数乘法广泛用于机器学习（ML）应用中。如实验所证实的，降低计算复杂度提高了计算速度，减少了硬件面积（执行计算的芯片的大小和成本），并且降低了功率。这个项目的目标是开发一个设计工具集，用于自动生成一元计算电路。此外，还将开发一个高度优化的硬件知识产权模块库。该研究的目标包括开发必要的工具集和设计流程，以帮助设计人员将拟议的技术集成到他们的电子设计中。该奖项反映了NSF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。

项目成果

Optimizing Hybrid Binary-Unary Hardware Accelerators Using Self-Similarity Measures

使用自相似性测量优化混合二元-一元硬件加速器

• DOI: 10.1109/fccm57271.2023.00020
• 发表时间: 2023
• 期刊: 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM
• 作者: Khataei, Alireza;Singh, Gaurav;Bazargan, Kia
• 通讯作者: Bazargan, Kia

Constant Coefficient Multipliers Using Self-Similarity-Based Hybrid Binary-Unary Computing

使用基于自相似性的混合二元-一元计算的常数系数乘法器

• DOI: 10.1109/iccad57390.2023.10323844
• 发表时间: 2023
• 期刊: 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD
• 作者: Khataei, Alireza;Bazargan, Kia
• 通讯作者: Bazargan, Kia

Approximate Hybrid Binary-Unary Computing with Applications in BERT Language Model and Image Processing

近似混合二元-一元计算及其在 BERT 语言模型和图像处理中的应用

• DOI: 10.1145/3543622.3573181
• 发表时间: 2023
• 期刊: FPGA '23: Proceedings of the 2023 ACM/SIGDA International Symposium on Field Programmable Gate Arrays
• 作者: Khataei, Alireza;Singh, Gaurav;Bazargan, Kia
• 通讯作者: Bazargan, Kia