  The ever increasing computational cost of Deep Neural Networks (DNN) and the
demand for energy efficient hardware for DNN acceleration has made accuracy and
hardware cost co-optimization for DNNs tremendously important, especially for
edge devices. Owing to the large parameter space and cost of evaluating each
parameter in the search space, manually tuning of DNN hyperparameters is
impractical. Automatic joint DNN and hardware hyperparameter optimization is
indispensable for such problems. Bayesian optimization-based approaches have
shown promising results for hyperparameter optimization of DNNs. However, most
of these techniques have been developed without considering the underlying
hardware, thereby leading to inefficient designs. Further, the few works that
perform joint optimization are not generalizable and mainly focus on CMOS-based
architectures. In this work, we present a novel pseudo agent-based
multi-objective hyperparameter optimization (PABO) for maximizing the DNN
performance while obtaining low hardware cost. Compared to the existing
methods, our work poses a theoretically different approach for joint
optimization of accuracy and hardware cost and focuses on memristive
crossbar-based accelerators. PABO uses a supervisor agent to establish
connections between the posterior Gaussian distribution models of network
accuracy and hardware cost requirements. The agent reduces the mathematical
complexity of the co-optimization problem by removing unnecessary computations
and updates of acquisition functions, thereby achieving significant speed-ups
for the optimization procedure. PABO outputs a Pareto frontier that underscores
the trade-offs between designing high-accuracy and hardware efficiency. Our
results demonstrate a superior performance compared to the state-of-the-art
methods both in terms of accuracy and computational speed (~100x speed up).
