Analog neural network chip with random weight change learning algorithm
Although researchers have been engaged in fabrication of neural network hardware, only a few networks implemented with a learning algorithm have been reported. A learning algorithm is required to be implemented on a VLSI chip because off-chip learning with a digital computer consumes too much time to be applied to many practical problems. The main obstacle to implement a learning algorithm is the complexity of the proposed algorithms. Algorithms like Back Propagation include complex multiplication, summation and derivatives, which are very difficult to implement with VLSI circuits. The authors propose a new learning algorithm, which is suitable for analog implementation, and implemented it on a 2.2 mm×2.2 mm neural network chip with 100 weights, using the standard 2.0 μm MOSIS process. The chips have successfully learned the XOR Gate problem.
Proceedings of the International Joint Conference on Neural Networks
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