FPGA-Based Accelerator Overview

Updated 24 May 2026

FPGA-based accelerators are reconfigurable hardware platforms that implement custom datapaths and memory hierarchies, enabling high parallelism for tasks like deep neural networks and sparse matrix computations.
They employ techniques such as pipelined processing, spatial parallelism, and adaptive memory scheduling to achieve significant throughput and energy efficiency improvements over conventional CPU/GPU systems.
Modern designs integrate heterogeneous computing elements and dynamic reconfigurability, supporting specialized ML, GNN, and transformer workloads while addressing resource bottlenecks and scalability challenges.

A field-programmable gate array (FPGA)-based accelerator is a reconfigurable hardware platform that implements custom datapaths and memory hierarchies to accelerate compute- and data-intensive workloads, notably deep neural networks (DNNs), sparse matrix problems, and dataflow-dominated applications. Designed for parallelism at the data, operation, and pipeline levels, FPGA accelerators exploit configurable logic blocks, abundant on-chip RAM (e.g., BRAM, URAM), high-bandwidth off-chip memory (e.g., HBM2), and application-specific hardware modules, often outperforming CPU/GPU systems in metrics such as energy efficiency and workload specialization (Yan et al., 2024, Jun, 2020, Petropoulos et al., 9 Oct 2025).

1. Architectural Fundamentals and Parallelism

FPGA accelerators instantiate custom, pipelined datapaths tailored to the fine- and coarse-grained characteristics of target algorithms. Canonical designs employ spatial parallelism via replicated processing elements (PEs), multi-stage pipeline structures, and deep on-chip buffer hierarchies to sustain throughput.

For CNN inference, (Jun, 2020) describes a three-dimensional pipeline spanning:

Input-channel parallelism: Multiple input channels processed concurrently.
Output-channel parallelism: Simultaneous computation across multiple output feature maps.
Convolution-window pipelining: A deep pipeline advances the sliding window per clock cycle.

High-level block diagram (simplified from (Jun, 2020)):

Input Buffer
      │
Window Buffer Module (K×K)
      │
Fully Parallel Multiply-Add-Tree (P×Q lanes)
      │
Output Accumulators
      │
Output Buffer

This structure leverages fully parallel multiply-add tree modules and window buffer pipelines to balance arithmetic throughput and memory bandwidth.

In (Petropoulos et al., 9 Oct 2025), the GEMM accelerator utilizes parameterized systolic arrays (SAs), partitioned HBM2 channels, and a pool of independently scheduled processing units (PUs). Each PU consists of pre-processing (activation buffers, im2col), systolic array for MACs, on-chip UltraRAM/BRAM for weights and biases, and post-processing (ReLU, accumulation).

2. Memory Hierarchy and Adaptive Utilization

FPGA-based accelerators are tightly coupled with both on-chip (BRAM, URAM) and off-chip (DDR4, HBM2) memory. Architecture design seeks to maximize data locality, bandwidth, and overlapped compute/memory operations:

In (Petropoulos et al., 9 Oct 2025), HBM is partitioned per PU, distributing activation, weight, and residual traffic over independent channels; adaptive memory scheduling dynamically overlaps HBM→URAM transfers with computation to minimize stalls.
Window-buffer pipelines (Jun, 2020) and double-buffering (Cong et al., 2018) support streaming access patterns, reduce DRAM latency overheads, and enable pipelined data loading and output.

Memory mapping and dataflow optimizations, such as cyclic weight storage for efficient access in transposed/normal modes (Venkataramanaiah et al., 2019), further accommodate both convolution forward/backward passes in training accelerators.

3. Resource Allocation, Performance Models, and Efficiency

FPGA resource utilization is constrained primarily by DSP slices (MAC engines), LUTs for control/logic, and available BRAM. Designs trade-off among P (input parallelism), Q (output parallelism), and K (kernel size) to saturate available compute while avoiding memory bottlenecks:

(Jun, 2020) achieves 317.86 GOPS at 32.73 GOPS/W on an Altera Cyclone V (P×Q lanes, 100% DSP utilization, 6% BRAM).
(Petropoulos et al., 9 Oct 2025) scales throughput near-linearly with the number of PU instances until URAM or DSPs saturate, with up to 10 PUs on AMD Alveo U50 (URAM 100%, 65% DSPs).

Performance modeling leverages throughput formulas:

$\mathrm{Throughput} = P \cdot Q \cdot K^2 \cdot f_{\mathrm{clk}}$

and energy efficiency as GOPS/W:

$\mathrm{EER} = \frac{\mathrm{Throughput\; (GOPS)}}{\mathrm{Power\; (W)}}$

FPGA implementations consistently report higher energy efficiency than GPU/CPU baselines for equivalent workloads (Yan et al., 2024, Jun, 2020).

DSP and memory bandwidth are principal bottlenecks as parallelism is scaled. Window buffer and addition tree optimizations reduce adder/register counts, decreasing dynamic power and enabling higher arithmetic and memory utilization at iso-resource.

4. Heterogeneity, Reconfigurability, and Software–Hardware Codesign

Modern designs increasingly exploit FPGA heterogeneity:

N³H-Core (Gong et al., 2021): Integrates DSP-based bit-parallel GEMM and LUT-based bit-serial GEMM on the same chip, supporting heterogeneous quantization and layer partitioning under unified programmable control (128-bit ISA).
Reconfigurable engines (Shao et al., 2024, Liu et al., 2023): Process a mixture of convolution (CNN), multi-head attention (Transformer), depthwise/pointwise convolutions, and nonlinear activations by dynamically time-multiplexing PE sub-structures (e.g., reconfigurable pipelines for attention vs. convolution).

Joint software–hardware design techniques include reinforcement learning for resource mapping, quantization, and module scheduling (Gong et al., 2021). Automatic RTL compilers (Venkataramanaiah et al., 2019) translate high-level CNN descriptions to synthesizable Verilog, performing DSE for tiling/unrolling.

5. Scalability and System Integration

FPGA-based accelerators are increasingly deployed within multi-instance, multi-tenant computing platforms:

UltraShare (Rezaei et al., 2019): Hardware controller supporting dynamic accelerator sharing, command-based interface, multi-queue grouping, and fair arbitration, yielding up to 8× throughput improvement under simultaneous multi-application workloads.
Scalable NoC integrations (Lin et al., 2020): Distributed and hierarchical packet sender/receiver designs decouple critical path from the number of accelerators, enabling near-linear scaling in on-chip accelerator integration, with chaining to reduce off-chip traffic.
All-SRAM spatial arrays (Parthasarathy, 15 Sep 2025): Implement grid-of-tile architectures (RISC-V microcores + FPU, scratchpad, message passing over a 2D-torus NoC) for high-bandwidth, memory-bound sparse linear algebra. The arithmetic intensity and on-chip bandwidth enable orders-of-magnitude acceleration for sparse iterative solvers compared to GPU/CPU approaches.

System-level design decisions address initialization and host integration costs (e.g., ~3 s bitstream load, <2.2 ms/event validation loop in firmware validation (Mizuhiki et al., 24 Mar 2025)), rapid iteration, and compatibility with standard EDA/driver toolchains.

6. Workload Specialization: ML, Sparse, and Heterogeneous Models

ML inference acceleration constitutes over 80% of recent research activity in the FPGA accelerator literature (Yan et al., 2024), with strong emphasis on CNNs, but also rapid expansion into GNNs and transformer models:

CNNs: Pipeline and systolic architectures dominate due to the regularity of dense matrix/tensor computations.
Transformers: Hybrid and reconfigurable designs (Shao et al., 2024, Liu et al., 2023) address convolution-attention fusion, nonlinear function approximation (Softmax, GELU), and quantized matrix multipliers.
GNNs: Compression and tiling techniques (PCOO), quantized arithmetic, and PE-load balancing enable efficient sparse-dense computation (Tao et al., 2021).
Sparse linear algebra: All-on-chip scratchpad arrays and explicit message-passing (SuperUROP (Parthasarathy, 15 Sep 2025)) overcome the irregular memory-access patterns that hinder traditional hardware.

Custom precision support, mixed-precision and dynamic quantization, and heterogeneous compute (e.g., INT8/2b (Srinivasan et al., 2019), hybrid DSP/LUT (Gong et al., 2021)) are leveraged for energy and throughput gains.

7. Limitations, Bottlenecks, and Future Directions

Despite demonstrated performance, FPGAs remain constrained by:

DSP slice count and BRAM/URAM limits, which cap island-level parallelism and working set size (Jun, 2020, Petropoulos et al., 9 Oct 2025).
On-chip/off-chip memory bandwidth, especially for large network models and training workloads; future HBM3 and multi-FPGA clusters are active research foci (Petropoulos et al., 9 Oct 2025, Yan et al., 2024).
Bitstream compilation and place–route inertia for extremely large or dynamic workloads.
Automation and programmability: While strides have been made in HLS flows and compiler RTL generation (Cong et al., 2018, Venkataramanaiah et al., 2019), further advances are needed for fully automated model-to-hardware pipelines at the scale and maturity of GPU ML frameworks.

Continued research explores adaptive precision, dynamic resource allocation, co-design for sparsity and fast transforms (e.g., Winograd, FFT), integration of analog in-memory computing (AIMC) emulation units (Petropoulos et al., 9 Oct 2025), robust support for training and ultra-low-latency inference (Yan et al., 2024), and interoperability among FPGAs, CPUs, AI engines, and networked storage.

References

(Jun, 2020) FPGA deep learning acceleration based on convolutional neural network
(Petropoulos et al., 9 Oct 2025) A Scalable FPGA Architecture With Adaptive Memory Utilization for GEMM-Based Operations
(Gong et al., 2021) N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Cores
(Parthasarathy, 15 Sep 2025) SuperUROP: An FPGA-Based Spatial Accelerator for Sparse Matrix Operations
(Yan et al., 2024) A survey on FPGA-based accelerator for ML models
(Rezaei et al., 2019) UltraShare: FPGA-based Dynamic Accelerator Sharing and Allocation
(Srinivasan et al., 2019) High Performance Scalable FPGA Accelerator for Deep Neural Networks
(Mizuhiki et al., 24 Mar 2025) Development of the firmware logic validation system using the FPGA accelerator
(Shao et al., 2024) An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
(Tao et al., 2021) LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator
(Cong et al., 2018) Best-Effort FPGA Programming: A Few Steps Can Go a Long Way
(Guo et al., 2017) A Survey of FPGA-Based Neural Network Accelerator