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A leap forward in compute-in-memory system for neural network inference

Authors: Liang Chu; Wenjun Li

DOI: 10.1088/1674-4926/25020028Status: Verified Translated Edition
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Key Findings in This Report

• AnDi architecture integrates analogue and digital computing cores to overcome limitations of analogue CIM in handling complex regression tasks requiring precise floating-point calculations. • The dual-domain floating-point (DDFP) processor enables FP compatibility and decouples NN algorithms from hardware, allowing training without considering specific architectures. • Fine-grained dual-domain mapping optimizes weight allocation between digital and analogue cores, improving energy efficiency and reducing memory management overhead. • NN feature-enhancing technique mitigates noise accumulation in analogue CIM by deploying lightweight enhancing layers on digital cores.