Toward Thermodynamic Reservoir Computing: Exploring SHA-256 ASICs as Potential Physical Substrates

TL;DR

This paper proposes CHIMERA and the Holographic Reservoir Computing framework to repurpose obsolete SHA-256 ASICs for physical reservoir computing by exploiting thermodynamic timing dynamics under voltage stress. It argues, with theoretical support, that a Hierarchical Number System representation could yield $E_{HNS} ∝ O( ext{log}\,n)$ energy scaling versus the von Neumann $E_{vN} ∝ O(2^n)$, suggesting potentially orders-of-magnitude efficiency gains if validated. Preliminary timing observations under edge-of-stability conditions show non-Poissonian inter-arrival variability, a phenomenon labeled the Silicon Heartbeat, but validation requires spectral analysis and rigorous experimentation. If proven, this approach could enable edge neuromorphic processing and hardware security primitives while promoting circular economy computing by giving obsolete mining ASICs a new computational role. The work remains theoretical with a concrete experimental program to confirm or refute the CHIMERA hypothesis, emphasizing clear distinctions between prediction and measurement and advocating open collaboration for replication and validation.

Abstract

We propose a theoretical framework--Holographic Reservoir Computing (HRC)--which hypothesizes that the thermodynamic noise and timing dynamics in voltage-stressed Bitcoin mining ASICs (BM1366) could potentially serve as a physical reservoir computing substrate. We present the CHIMERA (Conscious Hybrid Intelligence via Miner-Embedded Resonance Architecture) system architecture, which treats the SHA-256 hashing pipeline not as an entropy source, but as a deterministic diffusion operator whose timing characteristics under controlled voltage and frequency conditions may exhibit computationally useful dynamics. We report preliminary observations of non-Poissonian variability in inter-arrival time statistics during edge-of-stability operation, which we term the "Silicon Heartbeat" hypothesis. Theoretical analysis based on Hierarchical Number System (HNS) representations suggests that such architectures could achieve O(log n) energy scaling compared to traditional von Neumann O(2^n) dependencies. However, we emphasize that these are theoretical projections requiring experimental validation. We present the implemented measurement infrastructure, acknowledge current limitations, and outline the experimental program necessary to confirm or refute these hypotheses. This work contributes to the emerging field of thermodynamic computing by proposing a novel approach to repurposing obsolete cryptographic hardware for neuromorphic applications.