Quantized Attention Residuals: Ultra-Low Precision for Memory-Efficient Transformers

TL;DR: Can we make Attention Residuals even more memory- and compute-friendly by quantizing the attention weights and/or the layer outputs involved in the aggregation? By leveraging recent advances in quantization, this approach could bring AttnRes to edge devices or massive-scale deployments. An initial study would evaluate the tradeoff between precision, stability, and performance.

Research Question: How do different quantization schemes applied to Attention Residuals affect model stability, memory usage, and downstream performance—especially in resource-constrained environments?

Hypothesis: Carefully designed quantization (e.g., per-group or per-block) will enable substantial memory savings with minimal loss in accuracy for AttnRes models, and potentially even regularize training by smoothing out noisy layer contributions.

Experiment Plan: Implement quantized AttnRes aggregations with varying bit widths (e.g., 8-bit, 4-bit, per-group). Compare to full-precision AttnRes and standard residuals on downstream tasks and scaling experiments. Monitor hidden-state growth, gradient statistics, and memory/compute savings. Evaluate on both large-scale and edge-friendly transformer architectures.

References:

• Chen, K. et al. (2026). Attention Residuals.
• Bondarenko, Y., Nagel, M., & Blankevoort, T. (2021). Understanding and Overcoming the Challenges of Efficient Transformer Quantization. Conference on Empirical Methods in Natural Language Processing.
• Lee, C., & Lee, S. (2023). Softmax Output Approximation for Activation Memory-Efficient Training of Attention-based Networks. Neural Information Processing Systems.