Collaborative Research: SHF: Medium: Automated energy-efficient sensor data winnowing using native analog processing

随着计算在日常生活的各个方面变得普遍，计算硬件必须允许与物理世界的交互增加。这种相互作用采取本质上是模拟的感测信号的形式，例如，传感器可以输出可以呈现连续范围的值的电压。传统的主流计算将这些数据数字化，将其转换为数字0和1，以进行有效的分析和处理。然而，随着感测到的模拟数据量呈指数级增长，数字处理器将面临来自外部传感器的数据洪流。 对于这些大量的数据，甚至在任何处理之前将模拟输入数据转换为数字信号的成本也可能非常昂贵。 本机模拟处理（NAP）通过在模拟域中工作而无需模数转换。NAP可以用来实现数据处理功能，成本低廉，但只能达到有限的精度;另一方面，数字处理可以达到很高的精度，但需要模数转换的开销。这个项目提出了一种混合信号处理的方法，混合数字和模拟子电路实现，以实现两全其美。该项目旨在积极与半导体行业合作，培养半导体设计领域的研究生和本科生，从而缓解国家在这一领域的技能短缺。第一步，计算任务自动划分为混合模拟/数字部分，目标是满足系统级的吞吐量，功耗和噪声/错误限制。与任务相关的计算抽象地表示为一个图（DFG）。这种表示被广泛用于对各种任务进行建模，包括数字信号处理和机器学习中常用的任务。DFG中的节点被映射到模拟或数字实现，使用表示实现成本的成本函数，以及任何所需的模数或数模转换的成本。接下来，模拟和数字电路被优化，以基于尖端的晶体管技术在布局级别构建硅实现，这涉及到限制性的设计规则，例如单向布线和网格布局。该项目中开发的后端分析、综合和实施的新技术促进了优化，包括晶体管和互连优化、专门针对混合信号设计的布局和布线技术，以及高效预测电路性能的紧凑性能基于机器学习的模型生成。因此，该项目自动将计算任务的系统级DFG规范转换为优化的混合信号硅实现。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。

As computing becomes pervasive in all aspects of daily life, computing hardware must allow for increasing interaction with the physical world. This interaction takes the form of sensed signals that are analog in nature, e.g., a sensor may output a voltage that can take on a continuous range of values. Traditional mainstream computing digitizes this data, converting it to digital 0s and 1s for efficient analysis and processing. However, as the amount of sensed analog data is growing exponentially, digital processors will be faced with a data deluge from external sensors. For these vast volumes of data, even the cost of converting analog input data to digital signals, prior to any processing, can be prohibitively expensive. Native analog processing (NAP) negates the need for analog-to-digital conversion by working in the analog domain. NAP can be used to implement data processing functions inexpensively, but can achieve only limited accuracy; on the other hand, digital processing can achieve high accuracy, but requires the overhead of analog-to-digital conversion. This project presents a methodology for mixed-signal processing that hybridizes digital and analog subcircuit implementations to achieve the best of both worlds. The effort intends to actively engage with the semiconductor industry, and will train graduate and undergraduate students in the area of semiconductor design, thus alleviating the national skills shortage in this area.In the first step, computing tasks are automatically partitioned into hybrid analog/digital segments, with the goal of meeting system-level constraints on throughput, power, and noise/error. The computations associated with a task are abstractly represented by a dataflow graph (DFG). This representation is widely used to model a variety of tasks, including those commonly used in digital signal processing and machine learning. The nodes in the DFG are mapped to analog or digital implementations, using cost functions that represent the cost of implementation, as well as the cost of any required analog-to-digital or digital-to-analog conversion. Next, the analog and digital circuitry is optimized to build a silicon implementation at the layout level, based on cutting-edge transistor technologies, which involve restrictive design rules that impose limitations such as unidirectional routing and gridded layout. The optimizations are facilitated by novel techniques for back-end analysis, synthesis, and implementation developed in this project, including transistor and interconnect optimizations, placement and routing techniques that are specifically targeted to mixed-signal designs, and compact performance machine-learning-based model generation that efficiently predicts circuit performance. The project thus automatically translates the system-level DFG specification of a computing task to an optimized mixed-signal silicon implementation.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.