Neuromorphic Bayesian Optimization in Lava

• URL: http://arxiv.org/abs/2305.11060v1
• Date: Thu, 18 May 2023 15:54:23 GMT
• Title: Neuromorphic Bayesian Optimization in Lava
• Authors: Shay Snyder (1), Sumedh R. Risbud (2), and Maryam Parsa (1) ((1) George Mason University, (2) Intel Labs)
• Abstract summary: We introduce Lava Bayesian Optimization (LavaBO) as a contribution to the open-source Lava Software Framework. LavaBO is the first step towards developing a BO system compatible with heterogeneous, fine-grained parallel, in-memory neuromorphic computing architectures. We evaluate the algorithmic performance of the LavaBO system on multiple problems such as training state-of-the-art spiking neural network through back-propagation and evolutionary learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ever-increasing demands of computationally expensive and high-dimensional problems require novel optimization methods to find near-optimal solutions in a reasonable amount of time. Bayesian Optimization (BO) stands as one of the best methodologies for learning the underlying relationships within multi-variate problems. This allows users to optimize time consuming and computationally expensive black-box functions in feasible time frames. Existing BO implementations use traditional von-Neumann architectures, in which data and memory are separate. In this work, we introduce Lava Bayesian Optimization (LavaBO) as a contribution to the open-source Lava Software Framework. LavaBO is the first step towards developing a BO system compatible with heterogeneous, fine-grained parallel, in-memory neuromorphic computing architectures (e.g., Intel's Loihi platform). We evaluate the algorithmic performance of the LavaBO system on multiple problems such as training state-of-the-art spiking neural network through back-propagation and evolutionary learning. Compared to traditional algorithms (such as grid and random search), we highlight the ability of LavaBO to explore the parameter search space with fewer expensive function evaluations, while discovering the optimal solutions.

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