8 citations · 21 across the 22 of their papers we have counts for
22 papers
E-Graphs as a Persistent Compiler Abstraction
Jules Merckx, Alexandre Lopoukhine, Samuel Coward +3
Recent algorithmic advances have made equality saturation an appealing technique for program optimization, avoiding the phase-ordering problem by separating the discovery of equiva…
DéjàQ: Open-Ended Evolution of Diverse, Learnable and Verifiable Problems
Willem Röpke, Samuel Coward, Andrei Lupu +3
Recent advances in reasoning models have yielded impressive results in mathematics and coding. However, most approaches rely on static datasets, which have been suggested to encour…
eqsat: An Equality Saturation Dialect for Non-destructive Rewriting
Jules Merckx, Alexandre Lopoukhine, Samuel Coward +3
With recent algorithmic improvements and easy-to-use libraries, equality saturation is being picked up for hardware design, program synthesis, theorem proving, program optimization…
ReducedLUT: Table Decomposition with "Don't Care" Conditions
Oliver Cassidy, Marta Andronic, Samuel Coward +1
Lookup tables (LUTs) are frequently used to efficiently store arrays of precomputed values for complex mathematical computations. When used in the context of neural networks, these…
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
Davide Paglieri, Bartłomiej Cupiał, Samuel Coward +10
Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities, however, they still struggle to perform well i…
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL
Eduardo Pignatelli, Johan Ferret, Tim Rockäschel +4
The temporal credit assignment problem is a central challenge in Reinforcement Learning (RL), concerned with attributing the appropriate influence to each actions in a trajectory f…
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…
On the Systematic Creation of Faithfully Rounded Commutative Truncated Booth Multipliers
Theo Drane, Samuel Coward, Mertcan Temel +1
In many instances of fixed-point multiplication, a full precision result is not required. Instead it is sufficient to return a faithfully rounded result. Faithful rounding permits…
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…
JaxUED: A simple and useable UED library in Jax
Samuel Coward, Michael Beukman, Jakob Foerster
We present JaxUED, an open-source library providing minimal dependency implementations of modern Unsupervised Environment Design (UED) algorithms in Jax. JaxUED leverages hardware…
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…
Multiplier Optimization via E-Graph Rewriting
Andy Wanna, Samuel Coward, Theo Drane +2
Multiplier circuits account for significant resource usage in datapath-dominated circuit designs, and RTL designers continue to build bespoke hand-crafted multiplication arrays for…
SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR
Jianyi Cheng, Samuel Coward, Lorenzo Chelini +2
High-level synthesis (HLS) is a process that automatically translates a software program in a high-level language into a low-level hardware description. However, the hardware desig…
Datapath Verification via Word-Level E-Graph Rewriting
Samuel Coward, Emiliano Morini, Bryan Tan +2
Formal verification of datapath circuits is challenging as they are subject to intense optimization effort in the design phase. Industrial vendors and design companies deploy equiv…
Automating Constraint-Aware Datapath Optimization using E-Graphs
Samuel Coward, George A. Constantinides, Theo Drane
Numerical hardware design requires aggressive optimization, where designers exploit branch constraints, creating optimization opportunities that are valid only on a sub-domain of i…
Small Proofs from Congruence Closure
Oliver Flatt, Samuel Coward, Max Willsey +2
Satisfiability Modulo Theory (SMT) solvers and equality saturation engines must generate proof certificates from e-graph-based congruence closure procedures to enable verification…
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…
Combining E-Graphs with Abstract Interpretation
Samuel Coward, George A. Constantinides, Theo Drane
E-graphs are a data structure that compactly represents equivalent expressions. They are constructed via the repeated application of rewrite rules. Often in practical applications,…
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…
Abstract Interpretation on E-Graphs
Samuel Coward, George A. Constantinides, Theo Drane
Recent e-graph applications have typically considered concrete semantics of expressions, where the notion of equivalence stems from concrete interpretation of expressions. However,…
Attention-Based Clustering: Learning a Kernel from Context
Samuel Coward, Erik Visse-Martindale, Chithrupa Ramesh
In machine learning, no data point stands alone. We believe that context is an underappreciated concept in many machine learning methods. We propose Attention-Based Clustering (ABC…