arXiv subjects

Mohd Tasleem Khan

Publications and source records attributed to Mohd Tasleem Khan.

3 recordsLinked to original sources

arXiv2026

SCALE-X: A Systematic Complexity-Aware Low-Precision Approach for Digital Predistortion

This letter presents SCALE-X, a systematic, complexity-aware, low-precision, model-based digital predistortion (DPD) technique for power amplifiers with enhanced capabilities. The proposed approach employs a novel complexity-aware coefficient pruning method, which, when combined with optimized fixed-point modeling, enables more accurate capture of quantization effects and supports better informed decisions for implementation. Targeted configuration of model order, memory depth, and coefficient bitwidth delivers dramatic cuts in computational overhead without much sacrificing accuracy. Field-programmable gate array implementation demonstrates practical trade-offs with improved overall efficiency.

eess.SP↗

arXiv2026

COBALT: Column-swapping Optimized Bit-serial Accelerator for LSTM Tasks

Long Short-Term Memory (LSTM) networks continue to be widely deployed for real-time sequential tasks on edge devices, yet their computational demands challenge deployment on resource-constrained hardware. This work introduces COBALT, a bit-serial compressed LSTM accelerator built on a matrix (input)-vector (weight) reformulation of the standard circulant matrix-vector multiplication (MVM) with offset-binary coding. A novel column-swapping scheme operates on partial products at the bit level when generated in pairs, systematically exposing redundancy across output rows to reduce the number of PP generators and selectors. This redundancy is further exploited using a lightweight correction unit that derives a row's output directly from its paired row. Additionally, for block-circulant MVMs, relocating the shift-accumulate and correction units of each sub-MVM further reduces resource usage. The compressed network achieves weight compression of up to 93.6% while maintaining accuracy on the TIMIT and LibriSpeech-100h benchmarks. On a field-programmable gate array, COBALT achieves superior overall efficiency relative to state-of-the-art LSTM accelerators.

eess.SP↗

arXiv2026

COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques

Convolutional Neural Networks (CNNs) achieve remarkable accuracy in vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approach formulates CNN inference using OBC representations applied separately to inputs (Scheme A) and weights (Scheme B), enabling exploitation of bit-width asymmetry. The shift-accumulate operation is modified by incorporating offset-term with the pre-scaled bias. Leveraging symmetries in Schemes A and B, we introduce four look-up table (LUT) techniques -- parallel, shared, split, and hybrid -- and evaluate their efficiency. Building on this foundation, we develop a general matrix multiplication core using the im2col transformation for efficient CNN acceleration. We consider LeNet-5 and All-CNN-C to demonstrate that the OBC-GEMM core efficiently supports modern workloads. Evaluation shows that COMET enables efficient FPGA deployment compared to state-of-the-art designs, with negligible accuracy loss, demonstrating its efficiency and scalability across diverse network architectures.

eess.SP↗