Publications (22)
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
Michael Matthews, Michael Beukman, Benjamin Ellis +4
Benchmarks play a crucial role in the development and analysis of reinforcement learning (RL) algorithms. We identify that existing benchmarks used for research into open-ended lea…
Refining Minimax Regret for Unsupervised Environment Design
Michael Beukman, Samuel Coward, Michael Matthews +4
In unsupervised environment design, reinforcement learning agents are trained on environment configurations (levels) generated by an adversary that maximises some objective. Regret…
Automatic Datapath Optimization using E-Graphs
Samuel Coward, George A. Constantinides, Theo Drane
Manual optimization of Register Transfer Level (RTL) datapath is commonplace in industry but holds back development as it can be very time consuming. We utilize the fact that a com…
Automatic Generation of Complete Polynomial Interpolation Hardware Design Space
Bryce Orloski, Samuel Coward, Theo Drane
Hardware implementations of complex functions regularly deploy piecewise polynomial approximations. This work determines the complete design space of piecewise polynomial approxima…
ROVER: RTL Optimization via Verified E-Graph Rewriting
Samuel Coward, Theo Drane, George A. Constantinides
Manual RTL design and optimization remains prevalent across the semiconductor industry because commercial logic and high-level synthesis tools are unable to match human designs. Ou…
Combining Power and Arithmetic Optimization via Datapath Rewriting
Samuel Coward, Theo Drane, Emiliano Morini +1
Industrial datapath designers consider dynamic power consumption to be a key metric. Arithmetic circuits contribute a major component of total chip power consumption and are theref…