Model Compression and Hardware-Aware Acceleration for Deep Learning on FPGAs: A Co-Design Taxonomy and Comparative Analysis
Deploying deep neural networks on Field-Programmable Gate Arrays (FPGAs) requires joint reasoning about model compression and hardware acceleration, however the most comprehensive existing cross-platform treatment of this space, Deng et al.~\cite{deng2020model}, compared compression techniques against CPU, GPU, FPGA, and ASIC targets at the level of broad, qualitative trade-offs, and not specific FPGA resource consequences. This survey instead restricted the scope to FPGAs alone and organized 25 compression-hardware co-design case studies (2015--2026) into a five-category taxonomy defined by which FPGA resources each strategy primarily reshapes: DSP-eliminating, DSP-repurposing/mixed-precision, sparsity-exploiting, memory-hierarchy-driven, and toolchain/deployment-level. Normalizing these case studies along a common set of dimensions (compression ratio, accuracy change, throughput, energy efficiency, and DSP/LUT/BRAM utilization) surfaces a central, quantitative finding; of the 25 reviewed works, only \emph{one} reported a compression ratio and accuracy change measured against a single common baseline, and only \emph{two} reported energy efficiency normalized against a common GPU baseline, exposing a field-wide characterization gap that no individual toolchain (FINN, HLS4ML, Vitis AI, or DNNWeaver) resolves on its own. Building on this taxonomy and meta-analysis, we formalize six open challenges: toolchain fragmentation, accuracy--efficiency characterization, automated mixed-precision optimization, sparse computation reliability, persistent memory bottlenecks, and FPGA-based training. Each is paired with a concrete next step grounded in extending an existing, cited technique, not a general call for future work.