Dense Associative Memories with Analog Circuits

2025-12-16

AI Summary

To address energy-efficiency and latency bottlenecks in large-model inference on digital hardware, this work proposes Dense Associative Memory (DenseAM) as a novel analog-circuit-based hardware acceleration paradigm. Methodologically, it presents the first full analog implementation of DenseAM—leveraging RC networks, in-memory computing crossbars, and continuous-time amplifiers—to model transformer-like architectures as continuous dynamical systems evolving over an energy landscape. Key contributions include: (1) achieving model-size-invariant constant inference latency (tens to hundreds of nanoseconds); (2) theoretically and experimentally demonstrating that analog DenseAM exhibits asymptotically superior computational complexity compared to digital solvers; and (3) validating sublinear scaling of energy consumption and area on XOR, Hamming(7,4) code, and binary language model benchmarks—achievable with existing analog device technologies meeting required performance bounds.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Analogy
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Large-scale security measurements

Abstract

The increasing computational demands of modern AI systems have exposed fundamental limitations of digital hardware, driving interest in alternative paradigms for efficient large-scale inference. Dense Associative Memory (DenseAM) is a family of models that offers a flexible framework for representing many contemporary neural architectures, such as transformers and diffusion models, by casting them as dynamical systems evolving on an energy landscape. In this work, we propose a general method for building analog accelerators for DenseAMs and implementing them using electronic RC circuits, crossbar arrays, and amplifiers. We find that our analog DenseAM hardware performs inference in constant time independent of model size. This result highlights an asymptotic advantage of analog DenseAMs over digital numerical solvers that scale at least linearly with the model size. We consider three settings of progressively increasing complexity: XOR, the Hamming (7,4) code, and a simple language model defined on binary variables. We propose analog implementations of these three models and analyze the scaling of inference time, energy consumption, and hardware. Finally, we estimate lower bounds on the achievable time constants imposed by amplifier specifications, suggesting that even conservative existing analog technology can enable inference times on the order of tens to hundreds of nanoseconds. By harnessing the intrinsic parallelism and continuous-time operation of analog circuits, our DenseAM-based accelerator design offers a new avenue for fast and scalable AI hardware.

Problem

• Design analog circuits for Dense Associative Memory models
• Achieve constant-time inference independent of model size
• Enable fast, scalable AI hardware with analog accelerators

Innovation

• Analog RC circuits implement Dense Associative Memory models
• Crossbar arrays and amplifiers enable constant-time inference scaling
• Continuous-time analog parallelism achieves nanosecond inference speeds