Adaptive Noisy Neural Computation in Mixed-mode VLSI Hsin Chen, Patrice Fleury, Tong-Boon Tang and Alan Murray School of Engineering & Electronics, Edinburgh University, Mayfield Rd., Edinburgh, EH9 3JL, UK
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As the interest to integrate electronic technology with biological system grows, intelligent embedded systems become important to extract useful information from the bio-electrical interfaces. The intelligence refers to the ability to classify or recognize data while being robust to the noisy and drifting nature of biomedical signals. Despite the fact that various neural computational models have been shown able to deal with such signals, few models are actually hardware-friendly. The continuous-valued nature of the biomedical signals further requires complicated neural models, which impedes their amenability to embedded system. We propose a probabilistic neural computation which adapts its “internal noise” to code the natural variation of continuous-valued signals, and subsequently perform reliable classification or recognition. The probabilistic behaviour of the model relies on the noise injection at the input of each neuron/variable, and then enhances the robustness and representational power of the model. Since the learning algorithm requires merely additions and multiplications of one-step Gibbs’ sampled states, the full model can be implemented in mixed-mode VLSI circuits with on-chip learning. As proven by Matlab simulation, the learning algorithm is further simplified to fixed-step directional learning, rather than variable accurate-step learning. This significantly reduces the hardware complexity at a very low cost, i.e. a slightly slower convergence time. The Matlab simulation also showed that adapting the parameters in correct direction merely requires an accumulation of less than four training data. The I/O control of the circuits is thus minimized to allow the system for realistic applications. Following a brief introduction of the model, the circuit diagram (front-end design) of the model will be presented. As a full learning system has also been designed and fabricated, some preliminary results will be presented and discussed. Our previous works have shown that the model can classify real heartbeat data with 100% accuracy. In more recent experiments with the measured signals from multi-sensor microsystem, the model is further proved to be a robust classifier, as well as a reliable sensor-drift tracker. The VLSI implementation of the full model is thus a potential candidate for intelligent embedded system.
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