arXiv subjects

Biswajit Basu

Publications and source records attributed to Biswajit Basu.

6 recordsLinked to original sources

arXiv2026

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

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.

cs.AR↗

arXiv2026

Model Predictive Current Control with Harmonic Correction for Single-Phase AC-DC EV Charging

The increasing integration of Electric Vehicles (EVs) has imposed a growing harmonic challenge on the power grid. For AC/DC Power Factor Correction (PFC) in single-phase On-Board Chargers (OBCs), Model Predictive Current Control (MPCC) improves the current quality by predicting and tracking the inductor current. However, finite control set MPCC selects switching states, resulting in discrete control actions and a limited optimisation space. Moreover, the MPCC cost function based on instantaneous current tracking error has limited capability to compensate for low-order harmonic disturbances induced by dead time, control delay, and model parameter mismatch. This paper proposes a duty cycle predictive MPCC incorporating a real-time harmonic estimation reference. The proposed method dynamically estimates the low-order harmonic components of the input current and corrects the MPCC reference current, enabling continuous duty cycle control and targeted suppression of dominant low-order harmonics. Simulation results on a single-phase OBC demonstrate that the proposed duty cycle predictive MPCC reduces the steady-state current THD_i from 11.47% to 6.10% compared with the switching state predictive MPCC. With the harmonic reference, the THD_i is further reduced to 2.85%.

eess.SY↗

arXiv2025

LogicSparse: Enabling Engine-Free Unstructured Sparsity for Quantised Deep-learning Accelerators

FPGAs have been shown to be a promising platform for deploying Quantised Neural Networks (QNNs) with high-speed, low-latency, and energy-efficient inference. However, the complexity of modern deep-learning models limits the performance on resource-constrained edge devices. While quantisation and pruning alleviate these challenges, unstructured sparsity remains underexploited due to irregular memory access. This work introduces a framework that embeds unstructured sparsity into dataflow accelerators, eliminating the need for dedicated sparse engines and preserving parallelism. A hardware-aware pruning strategy is introduced to improve efficiency and design flow further. On LeNet-5, the framework attains 51.6 x compression and 1.23 x throughput improvement using only 5.12% of LUTs, effectively exploiting unstructured sparsity for QNN acceleration.

cs.AR↗

arXiv2024

Graph Expansion in Pruned Recurrent Neural Network Layers Preserve Performance

Expansion property of a graph refers to its strong connectivity as well as sparseness. It has been reported that deep neural networks can be pruned to a high degree of sparsity while maintaining their performance. Such pruning is essential for performing real time sequence learning tasks using recurrent neural networks in resource constrained platforms. We prune recurrent networks such as RNNs and LSTMs, maintaining a large spectral gap of the underlying graphs and ensuring their layerwise expansion properties. We also study the time unfolded recurrent network graphs in terms of the properties of their bipartite layers. Experimental results for the benchmark sequence MNIST, CIFAR-10, and Google speech command data show that expander graph properties are key to preserving classification accuracy of RNN and LSTM.

cs.LG↗

arXiv2020

On rotational flows with discontinuous vorticity beneath steady water waves near stagnation

We numerically investigate the flow structure of periodic steady water waves of fixed relative mass flux propagating on rotational flows with piece-wise constant vorticity. We show that for wave solutions along the global bifurcation diagram the stagnation point can first occur internally or at the bottom, and then again occurs at the crest with further increase in wave height. We observe that the bifurcation diagram has a new branch which is not connected to the trivial solution. Furthermore, we present an in-depth discussion of the results on pressure distributions and particle trajectories beneath large-amplitude steady waves near stagnation. We also expand on previous results concerning the amplitude and mass flux of steady water waves traveling on rotational flows with discontinuous vorticity.

physics.flu-dyn↗

arXiv2020

Capillary-Gravity Water Waves: Modified Flow Force Formulation

The classical irrotational capillary-gravity water wave problem described by the Euler equations with a nonlinear free surface boundary condition over a flat bed is considered. A modified flow force has been defined and a new formulation of capillary-gravity waves in the framework of the modified flow force function has been developed. Using bifurcation theory, the local existence of waves of small amplitude is proved.

math.AP↗