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 designs support only static-weight VMM, leaving nonlinear operations and dynamic matrix-matrix multiplicat
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 48%Algorithm–hardware co-design of neuromorphic networks with dual memory pathways →
- FuzzySimilar title/name (fuzzy) · 84%xorbitsai/inference →
“Fuzzy title match (0.92): “NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference” ≈ “xorbitsai/inference””
- LinkedLinked via arxiv author · 85%Jiajun Hu →
“NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference”
- LinkedLinked via arxiv author · 85%Ruthwik Reddy Sunketa →
“NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference”
- LinkedLinked via arxiv author · 85%Lei Zhao →
“NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference”
- LinkedLinked via arxiv author · 85%Archit Gajjar →
“NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference”
- LinkedLinked via arxiv author · 85%Luca Buonanno →
“NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference”
- LinkedLinked via arxiv author · 85%Daman Arora →
“NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference”