Neuromorphic computing systems execute machine learning tasks designed with spiking neural networks. These systems are embracing non-volatile memory to implement high-density and low-energy synaptic storage. Elevated voltages and currents needed to operate non-volatile memories cause aging of CMOS-based transistors in each neuron and synapse circuit in the hardware, drifting the transistor’s parameters from their nominal values. If these circuits are used continuously for too long, the parameter drifts cannot be reversed, resulting in permanent degradation of circuit performance over time, eventually leading to hardware faults. Aggressive device scaling increases power density and temperature, which further accelerates the aging, challenging the reliable operation of neuromorphic systems. Existing reliability-oriented techniques periodically de-stress all neuron and synapse circuits in the hardware at fixed intervals, assuming worst-case operating conditions, without actually tracking their aging at run-time. To de-stress these circuits, normal operation must be interrupted, which introduces latency in spike generation and propagation, impacting the inter-spike interval and hence, performance (e.g., accuracy). We observe that in contrast to long-term aging, which permanently damages the hardware, short-term aging in scaled CMOS transistors is mostly due to bias temperature instability. The latter is heavily workload-dependent and, more importantly, partially reversible. We propose a new architectural technique to mitigate the aging-related reliability problems in neuromorphic systems by designing an intelligent run-time manager (NCRTM), which dynamically de-stresses neuron and synapse circuits in response to the short-term aging in their CMOS transistors during the execution of machine learning workloads, with the objective of meeting a reliability target. NCRTM de-stresses these circuits only when it is absolutely necessary to do so, otherwise reducing the performance impact by scheduling de-stress operations off the critical path. We evaluate NCRTM with state-of-the-art machine learning workloads on a neuromorphic hardware. Our results demonstrate that NCRTM significantly improves the reliability of neuromorphic hardware, with marginal impact on performance.

No abstract

Picture a Scientist is a documentary film about the journey of three women scientists -Dr. Nancy Hopkins, Dr. Raychelle Burks, and Dr. Jane Willenbring, who faced some of the most brutal forms of harassment in their respective professions. The film presents alarming statistics about women in science who have experienced sexism, discrimination, microaggressions, and issues with the gender pay gap.The three scientists describe the long-lasting impact these harassment experiences have had on their mental health. The cumulative impact of consistent harassment is the same as that of one sexual assault incident (Picture a Scientist). This documentary offers an insight into the thought process of women while facing gender-based discrimination and the aftereffects of it. This insight can be valuable for mental health professionals aiming to support women in science.Dr. Nancy Hopkins was a molecular biologist and professor of biology at Massachusetts Institute of Technology (MIT) for forty years. During her undergraduate days, she worked in James Watson's lab. James Watson and Francis Crick won the Nobel Prize for discovering the helical structure of the DNA. (Nobelprize.org) During a visit from Crick, one that Dr. Hopkins was extremely excited for, he laid his hands on her breasts and inquired about her work. At the time, sexual assault was not something people talked about. So, to avoid embarrassing Crick, Hopkins stayed silent and pretended nothing had happened.During her time as a junior faculty member at MIT, Dr. Hopkins states in the film, she had to be accommodating and "nice" to doctoral students and other faculty who would take materials from her lab without permission, so as to not appear "difficult". When the administration refused to believe that her lab space as a senior faculty was smaller than the labs of some junior faculty, she took a tape measure and measured every lab in the department.