Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 2

2026-09-10

AI Summary

本文通过使用概率模拟内存计算处理器解决了大规模概率能量模型在GPU上执行时面临的可扩展性挑战，实现了超过1000倍的加速。

Abstract

This report extends our previous work (Part 1), which introduced an energy-based model for learning and decision-making under uncertainty. The model leverages stochastic Langevin dynamics to continuously evolve approximate probability distributions over neuron states and model weights. However, as noted in Part 1 and confirmed through GPU-based implementations, large-scale probabilistic energy-based models of this nature face significant scalability challenges due to excessive execution latency. This latency stems from a fundamental mismatch: massively parallel models with low arithmetic intensity (such as energy-based models) are being executed on processor architectures like GPUs that rely on high-bandwidth memory (HBM) interfaces. The HBM imposes brutally sequential execution constraints on inherently parallelizable models, creating the false impression that such models are unscalable. In reality, it is the GPU architecture itself, with its dependence on HBM interfaces, that is not a scalable processor architecture for this class of AI model. In this report, we demonstrate using a detailed transaction-level model (TLM) of a probabilistic analogue in-memory computing (AIMC) processor that the same energy-based model can execute well over 1000x faster than data-center-grade hardware by eliminating the HBM interface and performing computation directly within on-chip memory.

Problem

• scalability
• execution latency
• high-bandwidth memory
• probabilistic energy-based model
• GPU

Innovation

• probabilistic in-memory computing
• analogue in-memory computing (AIMC)
• high-bandwidth memory (HBM) interface
• energy-based model
• scalability