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EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems
Research article (Frontiers in Neuroscience, 2022) · cited 20× · AI/ML
EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems
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EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems is a scholarly article[1].
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EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems. Retrieved May 24, 2026, from https://4ort.xyz/entity/enforcesnn-enabling-resilient-and-energy-efficient-spiking-neural-network-inference-considering-approximate-drams-for-em
