An 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 backpropagation 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 implement it on a 2.2 mm/spl times/2.2 mm neural network chip with 100 weights, using the standard 2.0 /spl mu/m MOSIS process. The chips have successfully learned the XOR Gate problem.