CISE-ANR: SHF: Small: CHAMELEON: CompreHending And Mitigating Error in AnaLog ImplEmentations of On-Die Neural Networks

Considering the widespread deployment of machine-learning hardware in myriads of modern-life applications, ensuring its reliable and safe operation is crucial to advance the national prosperity and welfare and to secure the national defense. While software and/or digital hardware implementations of neural networks currently enjoy the lion’s share of the market, a number of emerging realities are necessitating the development and deployment of analog neural networks. Specifically, the exponential growth of sensory data from world-machine interfaces, known as the analog data deluge, along with the area, power consumption and response-time constraints of distributed edge-computing systems, necessitate autonomous sensing, perception, reasoning and rapid action. While analog neural-network implementations promise to deliver this ability, their robustness and reliability are susceptible to parametric differences introduced by manufacturing process variation, operational conditions variation, as well as silicon aging. Accordingly, this project seeks to enable robust and resilient operation of analog neural networks and the applications wherein they are deployed, as well as to educate the next generation of engineers on the risks and remedies of using analog machine-learning hardware. At a technical level, this project combines state-of-the-art methods for designing, testing, and calibrating analog integrating circuits, with advanced concepts from training machine-learning models, in an effort to comprehend the vulnerability of analog neural networks, develop error-mitigation solutions, and assess their effectiveness. To this end, the research activities undertaken by this project include (i) investigation and mitigation of the impact of parametric and operational differences on machine-learning models implemented as analog neural networks, (ii) development of methods for specifying and evaluating the learning capacity of such designs, and (iii) demonstration of the efficiency of the proposed methods through custom analog neural-network experimentation platforms. 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.