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Evidence record 11599 · automatically gathered

NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference

Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation. ReRAM-based analog in-memory computing (IMC) pushes efficiency further, offering an order-of-magnitude improvement in compute density and energy efficiency over conventional digital logic by performing vector-matrix multiplication (VMM) directly within the ReRAM crossbar; prior work has integrated such IMC blocks into FPGAs for DL inference. However, conventional IMC design

Record details

Published: 16 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Environment
Retrieved: 18 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (16 July 2026), “NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference,” evidence record 11599, https://ethics.ai/record/11599 (originally published by arXiv cs.AI).

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