A Heterogeneous Cluster with Reconfigurable Accelerator for Energy Efficient Near-Sensor Data Analytics

Abstract

IoT end-nodes require high performance and extreme energy efficiency to cope with complex near-sensor data analytics algorithms. Processing on multiple programmable processors operating in near-threshold is emerging as a promising solution to exploit the energy boost given by low-voltage operation, while recovering the related frequency degradation with parallelism. In this work, we present a heterogeneous cluster architecture extending a traditional parallel processor cluster with a reconfigurable Integrated Programmable Array (IPA) accelerator. While programmable processors guarantee programming legacy to easily manage peripherals, radio software stacks as well as the global program flow, offloading data-intensive and control-intensive kernels to the IPA leads to much higher system level performance and energy-efficiency. Experimental results show that the proposed heterogeneous cluster outperforms an 8-core homogeneous architecture by up to 4.8× in performance and 4.5× in energy efficiency when executing a mix of control-intensive and data-intensive kernels typical of near-sensor data analytics applications.