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

这一创新-技术转化伙伴关系(PFI-TT)项目的更广泛影响/商业潜力是利用“一元计算”显著提高选定的现代应用程序的性能。基于云的机器学习、高频交易以及用于安全摄像头、无人机(UAV)和视频流的视频/音频/信号处理就是此类应用的例子。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)和中等分辨率计算(8-12个二进制位)的功耗。作为降低电路功耗的直接结果，冷却成本也降低了。速度和成本是对行业成本有实际影响的重要计算机处理指标。许多现代计算机应用程序可以很容易地从这些改进中受益，该改进使用一种即插即用方案，其中用它们的一元对应物替换传统的功能实现，而不改变系统的其余部分。基于云的机器学习、高频交易以及用于安全摄像头、无人机(UAV)和视频流的视频/音频/信号处理就是此类应用的例子。拟议的项目开发了利用一元计算的设计方法，这是数字计算中一种新的、非常规的范例。用“未压缩”的数据表示法(一元)代替传统的二进制数字表示法，使计算非常高效。有限的硬件资源可以执行复杂的计算。传统的计算机编码方法通常需要许多乘法和加法运算，以通过多项式展开来计算函数，例如Cosh(X)、tanh(X)和y=x0.45。一元计算能够廉价而直接地执行这些计算，而不需要昂贵的乘法。这些功能广泛应用于信号处理(图像/视频/音频/雷达)中。此外，一元计算可以显著降低机器学习(ML)中广泛应用的常系数乘法的计算复杂度。通过实验验证，降低计算复杂度可以提高计算速度，减少硬件面积(执行计算的芯片的尺寸和成本)，降低功耗。这个项目的目标是开发一个设计工具集，用于自动生成用于一元计算的电路。此外，还将开发一个高度优化的硬件知识产权(IP)块库。这项研究的目标包括开发必要的工具集和设计流程，以帮助设计师将拟议的技术整合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智力优势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 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.