Abstract
This publication describes a structurally isomorphic architecture tailored for real-time inference in machine learning models. The architecture provides a dedicated pipelined structure that allows for the efficient execution of neural network computations by distributing tasks across discrete hardware units mapped to specific stages of a model. Features include synchronous operation, aggressive clock gating, and memory optimization based on capacity and throughput parameters. The design integrates compute capabilities directly with high-bandwidth memory stacks to reduce latency and power consumption.
Keywords: Isomorphic Architecture, Real-Time Inference, Hardware Pipeline, Clock Gating, In-Memory Computing, Large Language Models.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
N/A, "A Structurally Isomorphic Architecture For Real-Time Inference", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10042