RRAM Neuromorphic AI chip performance improved for edge computing

A group of international researchers able to improve RRAM based computer in memory (CIM) neuromorphic processor by using various technologies such as: voltage mode sensing of RRAM weights, interleaved replacement of CMOS neuron circuits with RRAM weights, using of hardware algorithm co-optimisation techniques to improve the reliability and power consumption of AI neuromorphic processor. To go into further details: The voltage mode sensing helps in achieving high parallelism and low power consumption, where all the columns and rows of RRAM array is activated in single computing cycle. Though they have not disclosed of using three-dimensional semiconductor fabrication but they could place CMOS neuron circuits interleaved with RRAM weights, calling it as transposable neurosynaptic array architecture, and non-ideality-aware model training and fine-tuning techniques. To achieve high versatility, they claim to have co-optimised all hierarchies of this chip design starting from hardware algorithms to architecture to circuits and devices. The performance and accuracy is hardware measured instead of simulating the device on software. NeuRRAM distributes processing loads parallel across 48 RRAM-CIM neuro-synaptic cores to achieve high versatility and efficiency. This chip achieves data parallelism by mapping a layer in the neural network model onto multiple codes for p...