MINIMALIST: switched-capacitor circuits for efficient in-memory computation of gated recurrent units
Sebastian Billaudelle, Laura Kriener, Filippo Moro, Tristan Torchet, Melika Payvand·May 13, 2025
Summary
A minimalist design introduces efficient GRUs for in-memory computing in hardware, targeting memory-constrained systems. It uses switched-capacitor circuits for computations and state updates, offering scalability and direct compatibility on edge computing for time series data. Benchmarks and simulations validate its efficiency. The design simplifies RNN models with binary output activations and reduced complexity, using a mixed-signal implementation for matrix-vector multiplications and state updates. It minimizes transitions between analog and digital domains, relying on specific components for straightforward scaling. Co-designed for optimal circuit-RNN function matching, it addresses constraints. The text highlights advancements in low-power RNN accelerators, showcasing works like "Eciton" and "Chipmunk" for real-time edge inference and near-sensor recurrent neural network inference. It explores RNN applications, energy-efficient multi-die acceleration, and in-memory computing for efficient machine learning.
Introduction
Background
Overview of GRUs and their role in in-memory computing
Importance of efficient GRUs in memory-constrained systems
Objective
To introduce a minimalist design for efficient GRUs in hardware, focusing on scalability and direct compatibility on edge computing for time series data
Method
Data Collection
Techniques for collecting time series data for edge computing
Data Preprocessing
Methods for preprocessing data to optimize GRU performance
Circuit Implementation
Design of switched-capacitor circuits for computations and state updates
Scalability of the circuit design for varying data sizes
Mixed-Signal Implementation
Utilization of mixed-signal techniques for matrix-vector multiplications and state updates
Minimization of transitions between analog and digital domains
Advancements in Low-Power RNN Accelerators
Eciton and Chipmunk
Detailed exploration of "Eciton" and "Chipmunk" for real-time edge inference
Comparison with minimalist GRU design in terms of efficiency and performance
RNN Applications and In-Memory Computing
RNN Applications
Overview of RNN applications in edge computing and near-sensor inference
Energy-Efficient Multi-Die Acceleration
Techniques for multi-die acceleration in RNNs to enhance energy efficiency
In-Memory Computing for Efficient Machine Learning
Integration of in-memory computing principles for efficient machine learning tasks
Benchmarks and Simulations
Validation of Efficiency
Results from benchmarks and simulations demonstrating the efficiency of the minimalist GRU design
Comparison with existing GRU implementations in terms of performance and resource utilization
Conclusion
Summary of Contributions
Recap of the minimalist GRU design's key features and benefits
Future Directions
Potential areas for further research and development in minimalist GRU hardware design
Basic info
papers
signal processing
hardware architecture
machine learning
artificial intelligence
Advanced features
Insights
In what ways does the design ensure direct compatibility with edge computing for time series data?
How does the minimalist design utilize switched-capacitor circuits for computations in memory-constrained systems?
What advancements in low-power RNN accelerators are highlighted in the design, particularly in relation to 'Eciton' and 'Chipmunk'?
What are the key benefits of using a mixed-signal implementation for matrix-vector multiplications in this design?