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REALIZATION OF NEURAL NETWORKS WITH TERNARY INPUTS AND TERNARY WEIGHTS IN NAND MEMORY ARRAYS
REALIZATION OF NEURAL NETWORKS WITH TERNARY INPUTS AND TERNARY WEIGHTS IN NAND MEMORY ARRAYS
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机译:NANR内存阵列中三元输入和三元权重的神经网络实现
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摘要
Use of a NAND array architecture to realize a binary neural network (BNN) allows for matrix multiplication and accumulation to be performed within the memory array. A unit synapse for storing a weight of a BNN is stored in a pair of series connected memory cells. A binary input is applied on a pair of word lines connected to the unit synapse to perform the multiplication of the input with the weight. The results of such multiplications are determined by a sense amplifier, with the results accumulated by a counter. The arrangement extends to ternary inputs to realize a ternary-binary network (TBN) by adding a circuit to detect 0 input values and adjust the accumulated count accordingly. The arrangement further extends to a ternary-ternary network (TTN) by allowing 0 weight values in a unit synapse, maintaining the number of 0 weights in a register, and adjusting the count accordingly.
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