MATLAB-Based Validation and Comparative Analysis of Quantum-Dot Transistor Compute-in-Memory Architecture for Neural Network Applications
Corresponding author: Abdulrahman Husawi
Abstract
This work provides independent validation and extended analysis of a Quantum-Dot Transistor (QDT) based Compute-in-Memory (CIM) architecture, without claiming silicon-level experimental verification. A comprehensive MATLAB-based simulation framework was developed to validate and analyze a previously proposed QDT-CIM architecture. The simulation implements device-level QDT models, circuit-level multiplier cells, and system-level memory arrays to perform matrix-vector multiplication operations essential for neural network inference. The validation study demonstrates strong agreement with the error rates reported in the original paper, with multiplication errors of approximately 0.1%, addition errors of 0.05%, and activation errors of 1.0%, yielding a total accumulated error of approximately 1.15%. The neural network classification experiments on synthetic MNIST-like data confirm the architectural advantages of QDT-based CIM, showing significant speedup over traditional von Neumann computing while maintaining competitive accuracy. Beyond replicating original results, this work contributes: (i) a reproducible modular simulation framework, (ii) systematic error-source decomposition identifying ADC quantization as the dominant error contributor, (iii) new analyses of device variability, IR-drop effects, and array scalability, and (iv) Quantization-Aware Training (QAT) achieving 96.8% accuracy on real MNIST data. Energy analysis estimates 0.5 pJ/MAC, while comparison with GPU implementations (~30 pJ/MAC) suggests potential improvement of up to 60×, although direct comparison requires consideration of technology node, precision, and workload differences. This work establishes a reproducible simulation framework for future research in neuromorphic computing systems.
Keywords:
compute-in-memory, quantum-dot transistor, MATLAB simulation, neural network, matrix-vector multiplication, validation study, neuromorphic computing, quantization-aware trainingReferences
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