NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference

The problem is that conventional ReRAM-based IMC blocks in FPGAs only support static-weight VMM, limiting efficiency gains for Transformer models that require nonlinear and dynamic matrix-matrix multiplication (DIMM) operations, while ADCs consume over 70% of IMC block area and power. The method proposes NIFA, a novel FPGA architecture integrating an ADC-free IMC block that replaces ADCs with analog content-addressable memories (ACAMs) to natively perform nonlinear operations, combined with FPGA-aware design-space exploration and an efficient mapping for DIMM. Experimental evidence shows up to 40x and 1.9x higher energy efficiency and 4.1x and 2.5x higher area efficiency on CNN and Transformer-based benchmarks, respectively. This matters because it significantly improves FPGA DL inference efficiency and sustains robust gains on Transformer-based workloads, advancing domain-specialized FPGA design for modern ML models.