RescueSNN: enabling reliable executions on spiking neural network accelerators under permanent faults.

To maximize the performance and energy efficiency of Spiking Neural Network (SNN) processing on resource-constrained embedded systems, specialized hardware accelerators/chips are employed. However, these SNN chips may suffer from permanent faults which can affect the functionality of weight memory and neuron behavior, thereby causing potentially significant accuracy degradation and system malfunctioning. Such permanent faults may come from manufacturing defects during the fabrication process, and/or from device/transistor damages (e.

EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems.

Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under unsupervised settings and low operational power/energy due to their bio-plausible computations. Previous studies identified that DRAM-based off-chip memory accesses dominate the energy consumption of SNN processing. However, state-of-the-art works do not optimize the DRAM energy-per-access, thereby hindering the SNN-based systems from achieving further energy efficiency gains.

SalvageDNN: salvaging deep neural network accelerators with permanent faults through saliency-driven fault-aware mapping.

Deep neural networks (DNNs) have proliferated in most of the application domains that involve data processing, predictive analysis and knowledge inference. Alongside the need for developing highly performance-efficient DNN accelerators, there is an utmost need to improve the yield of the manufacturing process in order to reduce the per unit cost of the DNN accelerators. To this end, we present 'SalvageDNN', a methodology to enable reliable execution of DNNs on the hardware accelerators with permanent faults (typically due to imperfect manufacturing processes).