RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs

2026-06-29

AI Summary

This work addresses the limitations of traditional high-granularity quantization (HGQ) in FPGA-based neural network compression, which relies on monotonic, irreversible layer-wise pruning that incurs substantial computational overhead and often fails to identify resource-constrained optimal subnetworks. To overcome this, the authors propose a resource-aware one-shot quantizer pruning method that directly maps the network into the target search space and integrates a bidirectional beta-scheduling fine-tuning strategy to efficiently explore the Pareto frontier between accuracy and hardware resource utilization. By eliminating progressive pruning, the approach reduces search cost by 20.58× compared to standard HGQ on jet substructure classification tasks while yielding a more competitive set of Pareto-optimal solutions and deployment configurations.

Abstract

High granularity quantisation (HGQ) exploits weight-level quantisation and pruning to design resource-efficient neural network accelerators, achieving an attractive trade-off between accuracy and hardware utilisation. HGQ is particularly well suited to FPGA-based edge neural network applications. Standard HGQ workflow starts from a high-precision model and progressively reduces bit width, guided by gradient-based optimisation to outline the Pareto frontier. This monotonic and irreversible pruning process is computationally intensive and can overlook the optimal subnetwork for a given resource level. We propose a resource-oriented one-shot quantiser pruning method that brings the network directly close to the target search space, and then use bidirectional beta scheduling for fine-tuning to enable a more refined scan of the Pareto frontier. Validated on the jet substructure classification, JSC, task, our method reduces the search cost by up to 20.58x compared with monotonic resource reduction in standard HGQ workflows, while achieving a competitive Pareto frontier and final network configuration.

Problem

• quantisation
• pruning
• FPGAs
• resource efficiency
• Pareto frontier

Innovation

• Resource-Oriented Pruning
• One-Shot Quantisation
• Bidirectional Beta Scheduling
• Pareto Frontier Exploration
• FPGA Neural Acceleration