Talk at EEAI2023


The lack of flexibility in most neuromorphic architecture designs results in significant performance loss and inefficient memory usage when mapping various neural network algorithms. We present SENECA, a digital neuromorphic architecture that balances the trade-offs between flexibility and efficiency using a hierarchical-controlling system. A SENECA core contains two controllers: a flexible RISC-V-based controller and an optimized controller (Loop Buffer). This flexible computational pipeline allows for deploying efficient mapping for various neural networks, on-device learning, and pre-post processing algorithms. The hierarchical-controlling system introduced in SENECA makes it one of the most efficient neuromorphic processors for event-driven neural network processing. In this talk, I will present the components of event-based neural network processing on SENECA, including detailed design space explorations and the optimized event-driven depth-first convolution. Further, I will present the benchmarking results on SENECA compared with state-of-the-art neuromorphic solutions and discuss how the research can benefit the future evolution of neuromorphic computing.

Oct 17, 2023 — Oct 19, 2023
Royal Olympic Hotel
28-34 Ath. Diakou Str., Athens, 11743
Guangzhi Tang
Guangzhi Tang
Assistant Professor

Edge AI, Robotics, Neuromorphic Computing, and AI-enabled Automation