Presentation
A Multi-objective Optimization Framework of Spiking Neural Network and Neuromorphic Processor
DescriptionLiquid State Machine (LSM), a spiking neural network model, has shown superiority in various applications due to its inherent spatiotemporal information processing property and low training complexity. Traditional hyperparameters optimization methodologies for LSM usually focus on the mono-criteria of accuracy while ignoring the trade-off among accuracy, parameter size, and hardware overhead (e.g., power consumption) when deployed on neuromorphic processors, which hinders LSM's better applications in resource-restricted scenarios (e.g., embedded systems). Thus, co-considering the performance of LSM algorithms and hardware constraints is critical for real-world applications, which still requires further exploration. This work treats the optimization of LSM as a Multi-objective Optimization Problem (MOP) and proposes a general hardware-aware multi-objective optimization framework. In light of the vast design space and time-consuming function evaluations of the spiking neural network, a decomposition-based Multi-objective Optimization Algorithm (MOA) aiming at computationally expensive problems, MOTPE/D, is proposed in this framework. Experiments are conducted on two typical case studies, i.e., N-MNIST classification and DVS-128 classification. It has been experimentally supported that the proposed framework outperforms peer solutions in terms of different performance indicators. This work is open sourced for reproducibility and further study, which can be accessed through: https://anonymous.4open.science/r/MOTPE-D.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security