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.

翻译：我们提出了一个理论框架——全息储层计算（HRC），该框架假设电压应力下的比特币挖矿专用集成电路（BM1366）中的热力学噪声与时序动态，可能作为一种物理储层计算基底。我们介绍了CHIMERA（通过矿机嵌入式共振架构实现的意识混合智能）系统架构，该架构将SHA-256哈希流水线并非视为熵源，而是作为一种确定性扩散算子，其在受控电压与频率条件下的时序特性可能展现出具有计算价值的动态行为。我们报告了在稳定性边缘运行期间，到达时间间隔统计中观察到的非泊松变异性的初步结果，我们将其称为“硅心跳”假说。基于分层数系（HNS）表示的理论分析表明，相较于传统冯·诺依曼架构的O(2^n)依赖关系，此类架构可能实现O(log n)的能量缩放。然而，我们强调这些均为需要实验验证的理论预测。我们介绍了已实现的测量基础设施，指出了当前局限，并概述了验证或反驳这些假说所需的实验方案。本研究通过提出一种将过时密码硬件重新用于神经形态应用的新颖方法，为新兴的热力学计算领域做出了贡献。