Analog In-Memory Computing Attention Mechanism for Fast and Energy-Efficient Large Language Models

• URL: http://arxiv.org/abs/2409.19315v2
• Date: Mon, 25 Nov 2024 12:14:33 GMT
• Title: Analog In-Memory Computing Attention Mechanism for Fast and Energy-Efficient Large Language Models
• Authors: Nathan Leroux, Paul-Philipp Manea, Chirag Sudarshan, Jan Finkbeiner, Sebastian Siegel, John Paul Strachan, Emre Neftci,
• Abstract summary: Transformer networks, driven by self-attention, are central to Large Language Models. In generative Transformers, self-attention uses cache memory to store token projections, avoiding recomputation at each time step. We present a custom self-attention in-memory computing architecture based on emerging charge-based memories called gain cells.
• Score: 0.755189019348525
• License:
• Abstract: Transformer networks, driven by self-attention, are central to Large Language Models. In generative Transformers, self-attention uses cache memory to store token projections, avoiding recomputation at each time step. However, GPU-stored projections must be loaded into SRAM for each new generation step, causing latency and energy bottlenecks. We present a custom self-attention in-memory computing architecture based on emerging charge-based memories called gain cells, which can be efficiently written to store new tokens during sequence generation and enable parallel analog dot-product computation required for self-attention. However, the analog gain cell circuits introduce non-idealities and constraints preventing the direct mapping of pre-trained models. To circumvent this problem, we design an initialization algorithm achieving text processing performance comparable to GPT-2 without training from scratch. Our architecture respectively reduces attention latency and energy consumption by up to two and five orders of magnitude compared to GPUs, marking a significant step toward ultra-fast, low-power generative Transformers.

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