Demonstrating Advanced Machine Learning and Neuromorphic Computing Using IBM’s NS16e

Abstract

The human brain can be viewed as an extremely power-efficient biological computer. As such, there have been many efforts to create brain-inspired processing systems to enable advances in low-power data processing. An example of brain-inspired processing architecture is the IBM TrueNorth Neurosynaptic System, a Spiking Neural Network architecture for deploying ultra-low power machine learning (ML) models and algorithms. For the first time ever, an advanced scalable computing architecture was demonstrated using 16 TrueNorth neuromorphic processors containing in aggregate over 16 million neurons. This system, called the NS16e, was used to demonstrate new ML techniques including the exploitation of optical and radar sensor data simultaneously, while consuming a fraction of the power compared to traditional Von Neumann computing architectures. The number of applications that have requirements for computing architectures that can operate in size, weight and power-constrained environments continues to grow at an outstanding pace. These applications include processors for vehicles, homes, and real-time data exploitation needs for intelligence, surveillance, and reconnaissance missions. This research included the successful exploitation of optical and radar data using the NS16e system. Processing performance was assessed, and the power utilization was analyzed. The NS16e system never used more than 15 W, with the contribution from the 16 TrueNorth processors utilizing less than 5 W. The image processing throughput was 16,000 image chips per second, corresponding to 1,066 image chips per second for each watt of power consumed.

Received and approved for public release by the Air Force Research Laboratory (AFRL) on 11 June 2019, case number 88ABW-2019-2928. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors, and do not necessarily reflect the views of AFRL or its contractors. This work was partially funded under AFRL’s Neuromorphic - Compute Architectures and Processing contract that started in September 2018 and continues until June 2020.