A Self-Learning Neural Network LSI Employing Random Noise Generator

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Generation of spatially and temporary random noise is essential in implementing intelligent information processing systems like neural networks as integrated circuits hardware. However, this has been a very difficult task. The voltage fluctuation appearing across the two terminals of a resistor due to the random motion of electrons is known as thermal noise, which is known as white noise. If such thermal noise is amplified to an appropriate level for circuit operation, it provides a very good noise source. However, the output is band limited due to the RC circuitry composed of the noise source resistor and the input capacitance of the amplifier, resulting in the available noise power of kT/C.This is only the order of mV for typical amplifiers. We resolved the problem by utilizing the "Anti-Miller Effect" of a source follower which we discovered. In a source follower the input capacitance becomes almost zero due to its almost unity gain. Further more CMOS inverter was utilized as a noise source resistor, yielding better whiteness of the noise. As a result, we developed a random-noise generator composed of only eight transistors and the performance has been experimentally verified. For neural processing, we have developed a retina processing chip which performs noise filtering, edge enhancement by two-dimensional Laplacian processing, and edge detection. The basic operation of the test circuit has been also verified experimentally. By injecting random noise to the pixel processing, we have found that a missing feature can be restored by stochastic resonance. In this manner, the important role of random noise generation in intelligent data processing has been demonstrated.