NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference

The problem is that conventional ReRAM-based analog in-memory computing (IMC) blocks in FPGAs only support static-weight vector-matrix multiplication, limiting efficiency gains for Transformer models that require frequent nonlinear and dynamic matrix-matrix multiplication (DIMM) operations, while the ADCs within each IMC block consume over 70% of its area and power. The method proposes a novel FPGA architecture integrating an ADC-free IMC block that replaces ADCs with analog content-addressable memories (ACAMs) to natively perform nonlinear operations, along with FPGA-aware design-space exploration and efficient mapping to enable DIMM operations for attention computation. Experimental evidence shows that on CNN and Transformer-based benchmarks, the architecture achieves up to 40x and 1.9x higher energy efficiency and 4.1x and 2.5x higher area efficiency, respectively. This matters because it significantly improves FPGA deep learning inference efficiency, particularly for Transformer-based workloads across long input sequences, advancing domain-specialized FPGA design.