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Found 3 papers, 1 papers with code
Analog, In-memory Compute Architectures for Artificial Intelligence
This paper presents an analysis of the fundamental limits on energy efficiency in both digital and analog in-memory computing architectures, and compares their performance to single instruction, single data (scalar) machines specifically in the context of machine inference.
Biologically Plausible Learning on Neuromorphic Hardware Architectures
Using the benchmarking framework DNN+NeuroSim, we investigate the impact of hardware nonidealities and quantization on algorithm performance, as well as how network topologies and algorithm-level design choices can scale latency, energy and area consumption of a chip.
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Although state-of-the-art (SOTA) CNNs achieve outstanding performance on various tasks, their high computation demand and massive number of parameters make it difficult to deploy these SOTA CNNs onto resource-constrained devices.
