ArchGym: An Open-Source Gymnasium for Machine Learning Assisted
  Architecture Design
Srivatsan Krishnan Amir Yazdanbaksh Shvetank Prakash Jason Jabbour Ikechukwu Uchendu Susobhan Ghosh Behzad Boroujerdian Daniel Richins Devashree Tripathy Aleksandra Faust Vijay Janapa Reddi
Machine learning is a prevalent approach to tame the complexity of design
space exploration for domain-specific architectures. Using ML for design space
exploration poses challenges. First, it's not straightforward to identify the
suitable algorithm from an increasing pool of ML methods. Second, assessing the
trade-offs between performance and sample efficiency across these methods is
inconclusive. Finally, lack of a holistic framework for fair, reproducible, and
objective comparison across these methods hinders progress of adopting ML-aided
architecture design space exploration and impedes creating repeatable
artifacts. To mitigate these challenges, we introduce ArchGym, an open-source
gym and easy-to-extend framework that connects diverse search algorithms to
architecture simulators. To demonstrate utility, we evaluate ArchGym across
multiple vanilla and domain-specific search algorithms in designing custom
memory controller, deep neural network accelerators, and custom SoC for AR/VR
workloads, encompassing over 21K experiments. Results suggest that with
unlimited samples, ML algorithms are equally favorable to meet user-defined
target specification if hyperparameters are tuned; no solution is necessarily
better than another (e.g., reinforcement learning vs. Bayesian methods). We
coin the term hyperparameter lottery to describe the chance for a search
algorithm to find an optimal design provided meticulously selected
hyperparameters. The ease of data collection and aggregation in ArchGym
facilitates research in ML-aided architecture design space exploration. As a
case study, we show this advantage by developing a proxy cost model with an
RMSE of 0.61% that offers a 2,000-fold reduction in simulation time. Code and
data for ArchGym is available at https://bit.ly/ArchGym.