Traditional Boolean computing methods are reaching their physical limits. Neuromorphic computing—a system engineered to mimic neuro-biological architectures present in the nervous system—has gained great attention as an alternative. Large scale neural networks consist of a massive number of synapses that connect between groups of neurons. This requires a large volume of memory devices, resulting in a tremendous hardware cost. Therefore, it is beneficial to develop more compact synaptic devices using emerging non-volatile memory devices such as resistive cross-point arrays. Unfortunately, the realistic properties of synaptic devices currently available, such as the limited ON/OFF range of resistive devices, could hamper the read accuracy, and thus the computation results.
Researchers at Arizona State University have invented a method to overcome the limited ON/OFF range of resistive devices. Resistive cross-point array architecture is implemented with a dummy column to correct for non-ideal ON/OFF ranges. Ideally, when a synapse is off, the off-state current should be zero. Unfortunately, this only occurs when the ratio between the maximum and minimum conductance (ON/OFF ratio) approaches infinity, which is not feasible in current resistive devices. This technology analyzes current from a dummy column together with the current from a particular synapse to eliminate the effect of the off-state current and artificially bring it to zero.
Potential Applications
Benefits and Advantages
For more information about the inventor(s) and their research, please see
Dr. Shimeng Yu's directory webpage
Dr. Yu Cao's directory webpage
Dr. Sarma Vrudhula's directory webpage