  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.
