NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference
Important hardware architecture research for ML researchers and engineers designing specialized accelerators, particularly relevant for improving transformer inference efficiency at the hardware level.
AI Summary
This arXiv paper introduces a novel FPGA architecture called NIFA (Nonlinear IMC enhanced FPGA) that integrates ReRAM-based analog in-memory computing blocks with analog content-addressable memories to perform nonlinear operations directly within memory cells, overcoming limitations of conventional digital FPGAs for model . The proposed hardware design eliminates power-hungry ADCs and enables more efficient attention computation, achieving up to 40× higher energy efficiency and 4.1× higher area efficiency for CNN/Transformer workloads. This represents foundational hardware architecture research for domain-specialized ML accelerators.
Excerpt
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