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Found 3 papers, 1 papers with code
Kernel Approximation using Analog In-Memory Computing
We introduce an approach to kernel approximation in machine learning algorithms suitable for mixed-signal Analog In-Memory Computing (AIMC) architectures.
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing
In this work, we pair L2L with in-memory computing NMHW based on phase-change memory devices to build efficient AI models that can rapidly adapt to new tasks.
A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing
Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference.
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