Harmonica: A Framework of Heterogeneous Computing Systems with Memristor-Based Neuromorphic Computing Accelerators

Following technology scaling, on-chip heterogeneous architecture emerges as a promising solution to combat the power wall of microprocessors. This work presents Harmonica - aframework of heterogeneous computing system enhanced by memristor-based neuromorphic computing accelerators (NCAs). In Harmonica, a conventional pipeline is augmented with a NCA which is designed to speedup artificial neural network (ANN) relevant executions by leveraging the extremely efficient mixed-signal computation capability of nanoscale memristor-based crossbar (MBC) arrays. With the help of a mixed-signal interconnection network (M-Net), the hierarchically arranged MBC arrays can accelerate the computation of a variety of ANNs. Moreover, an inline calibration scheme is proposed to ensure the computation accuracy degradation incurred by the memristor resistance shifting within an acceptable range during NCA executions. Compared to general-purpose processor, Harmonica can achieve on average 27.06 × performance speedup and 25.23 × energy savings when the NCA is configured with auto-associative memory (AAM) implementation. If the NCA is configured with multilayer perception (MLP) implementation, the performance speedup and energy savings can be boosted to 178.41 × and 184.24 ×, respectively, with slightly degraded computation accuracy. Moreover, the performance and power efficiency of Harmonica are superior to the designs with either digital neural processing units (D-NPUs) or MBC arrays cooperating with a digital interconnection network. Compared to the baseline of general-purpose processor, the classification rate degradation of Harmonica in MLP or AAM is less than 8% or 4%, respectively.

Duke Scholars

Author Hai "Helen" Li Electrical and Computer Engineering

Author Yiran Chen Electrical and Computer Engineering