Practical Low-Weight Codes for Energy-Efficient Bus Encoding

2026-06-12

AI Summary

This work proposes two novel low-complexity, near-optimal encoding schemes to reduce the Hamming distance between consecutive messages on data buses, thereby lowering energy consumption and extending the lifetime of non-volatile memory. The approach integrates a predefined random codebook with a suboptimal encoding strategy and derives a closed-form expression for the average number of bit flips. Under a configuration with 64-bit data words augmented by 8 redundant bits, the proposed schemes achieve approximately a 24.7% reduction in bit flips—closely approaching the theoretical optimum of 26.4% and significantly outperforming existing practical coding methods.

Abstract

We consider the transmission of data encoded into binary messages, with the goal of minimizing the Hamming distance, i.e., the number of bit-flips, between consecutive messages. This problem is relevant for enhancing the longevity of Non-Volatile Memories and reducing transition-induced energy consumption in data buses. Known as Write-Efficient Memory coding in the literature, this challenge has traditionally been addressed using optimal but complex schemes. In low-power computer systems the same topic is known as bus encoding. In this paper, we derive closed-form expressions to evaluate the average number of bit-flips for practical, sub-optimal encoding schemes, and propose two new schemes assisted by predefined random codebooks. We demonstrate that low-complexity solutions achieve performance very close to the optimal schemes, making them attractive for implementation in energy-sensitive and memory-critical applications. For instance, by adding 8 extra bits to 64-bits data, sub-optimal schemes can achieve a bit-flip reduction (related to energy saving) of approximately 24.7%, compared to the 26.4% reduction offered by the significantly more complex optimal scheme.

Problem

• Hamming distance
• bit-flips
• bus encoding
• Write-Efficient Memory
• energy consumption

Innovation

• bus encoding
• low-weight codes
• Hamming distance minimization
• energy-efficient communication
• sub-optimal coding