MLG-NCS: Multimodal Local-Global Neuromorphic Computing System for Affective Video Content Analysis

Abstract

Despite neuromorphic computing (NC) technologies offer tremendous potential in executing computationally intensive tasks with high efficiency and low latency, most of existing methods are still difficult to achieve software-comparable accuracy. To address this challenge, we develop a multimodal local-global NC system (MLG-NCS) that can capture local characteristics and exchange global cross-modal information sufficiently. Specifically, a high-density memristor crossbar array is prepared to perform efficient parallel in-memory operations, serving as the fundamental component of the proposed MLG-NCS. To facilitate understanding of the proposed MLG-NCS design, the local feature representation module, the global cross-modal interaction module, and the output module are designed. The experimental results show that the proposed system has advantages in classification accuracy (ranked top three), time consumption (approximately ten times speed up), and latency (about 1.2–15.3 times faster), enabling good inter-related tradeoffs between latency, efficiency, and accuracy. This study is expected to promote the revolution and development of next-generation computing system, which takes a firm step toward artificial general intelligence (AGI).