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), while ADCs consume over 70% of IMC block area and power. The method proposes a novel FPGA architecture with an ADC-free IMC block using analog content-addressable memories (ACAMs) for native nonlinear operations, along with FPGA-aware design-space exploration and 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 benchmarks, respectively. This matters because it significantly improves FPGA DL inference efficiency, especially for Transformer-based workloads, advancing domain-specialized FPGA design.