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Demonstration of 4-quadrant analog in-memory matrix multiplication in a single modulation
Manuel Le Gallo1, Oscar Hrynkevych1,2, Benedikt Kersting1
1IBM Research Europe, 8803 Rüschlikon, Switzerland.
Npj Unconventional Computing
|October 7, 2024
Summary
This study introduces a novel 4-quadrant matrix-vector multiplication (MVM) for analog in-memory computing (AIMC). The new method enhances accuracy and efficiency in phase-change memory chips for AI tasks.
Area of Science:
- Materials Science
- Computer Engineering
- Artificial Intelligence
Background:
- Analog in-memory computing (AIMC) uses resistive memory devices for computations like matrix-vector multiplications (MVMs).
- Single-phase MVMs reduce latency but suffer accuracy issues due to device conductance's voltage polarity dependence.
Purpose of the Study:
- To develop a 4-quadrant MVM technique mitigating conductance polarity dependence in AIMC.
- To implement and validate this technique on a multi-core phase-change memory (PCM) chip.
Main Methods:
- Developed analog and digital calibration procedures to address conductance polarity dependence.
- Implemented a 4-quadrant MVM in a single modulation scheme.
- Utilized a multi-core AIMC chip based on phase-change memory.
Main Results:
- Successfully demonstrated accurate neural network inference and similarity search tasks.
- Achieved 4 times higher MVM throughput compared to conventional four-phase reading schemes.
- Significantly improved energy efficiency in AIMC operations.
Conclusions:
- The developed calibration methods effectively mitigate conductance polarity dependence in AIMC.
- The 4-quadrant MVM approach enables efficient and accurate computation on PCM-based AIMC chips.
- This work advances AIMC for practical AI applications requiring high throughput and energy efficiency.
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