ArticleFrontiers in neuroscience2022
EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- SNN4Agents: a framework for developing energy-efficient embodied spiking neural networks for autonomous agents.Frontiers in robotics and AI · 2024Article
- RescueSNN: enabling reliable executions on spiking neural network accelerators under permanent faults.Frontiers in neuroscience · 2023Article
- Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms.Sensors (Basel, Switzerland) · 2021Article
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Abstract
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. To substantially reduce the DRAM energy-per-access, an effective solution is to decrease the DRAM supply voltage, but it may lead to errors in DRAM cells (i.e., so-called
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