Approximate Computing

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 16

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Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference
Oct 5, 2026Nathaniel Kaye Mellor, Shreejith Shanker, George FlorosEdge Computing Graph Sparsification
Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland
FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retraining
Sep 15, 2026Rappy Saha, Nima Amirafshar, Jude Haris +2FPGA-Based Neural Network Acceleration Approximate Computing
University of Glasgow, UK · Heidelberg University, Germany
GaLe: memory-efficient Global Approximate and Local Exact features
Sep 2, 2026Alberto Ancilotto, Elisabetta FarellaMemory-Efficient Inference Edge AI
Fondazione Bruno Kessler (FBK), Trento, Italy
CircuitsDNA: Discovering Unconventional Multi-Accuracy Arithmetic Circuits via Evolutionary Synthesis
Sep 1, 2026Ruichen Qi, Junyi Luo, Xinting Jiang +5Efficient Neural Network Inference Approximate Computing
Brown University · Google · University of Michigan
CurveFP: Co-Designing Numerical Representation and Product Arithmetic for Language Models
Aug 8, 2026Ye QiaoLLM Quantization Low-Bit Quantization
Department of Electrical Engineering and Computer Science University of California, Irvine Irvine, CA, USA
Toward Energy-Efficient and Low-Power Arrhythmia Detection for Wearable Devices
Jul 16, 2026Floriaan Bulten, Yawar Rasheed, Arlene John +2Energy-Efficient ML Electrocardiogram Classification
Computer Architecture for Embedded Systems, University of Twente, The Netherlands · Biomedical Signals and Systems, University of Twente, The Netherlands · Faculty of Science, Computer Science, Vrije Universiteit Amsterdam, The Netherlands
Harvesting AI Computation at the Edge via Generic Approximation
Jun 28, 2026Yihan Wang, Huiru Yan, Luxin Zhang +6Edge Computing Approximate Computing
Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing 100190, China · North China Electric Power University, Beijing 102206, China
Compiler-Driven Approximation Tuning for Hyperdimensional Computing
Jun 25, 2026Xavier Routh, Abdul Rafae Noor, Akash Kothari +4Hyperdimensional Computing Approximate Computing
University of Illinois Urbana-Champaign Urbana, IL, USA · University of California San Diego La Jolla, CA, USA
Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation
Jun 11, 2026Martin Tomasovic, Lukas SekaninaMulti-Objective Evolutionary Optimization Large Language Model-Guided Optimization
Brno University of Technology, Faculty of Information Technology Brno, Czech Republic
SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC
Jun 8, 2026Sonu Kumar, Akash Sankhe, Mukul Lokhande +1Edge Inference Privacy-Preserving ML
Centre for Advanced Electronics · Dept. of Electrical Engineering
Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design
May 20, 2026Ondrej Galeta, Lukas SekaninaElectronic Design Automation Genetic Programming
Brno University of Technology, Faculty of Information Technology, Brno, Czech Republic
A Reconfigurable Multiplier Architecture for Error-Resilient Applications in RISC-V Core
May 9, 2026Pragun Jaswal, L. Hemanth Krishna, B. SrinivasuEfficient Neural Network Inference Energy-Efficient ML
Indian Institute of Technology Mandi, Mandi–175005, India
CARMEN: CORDIC-Accelerated Resource-Efficient Multi-Precision Inference Engine for Deep Learning
May 7, 2026Sonu Kumar, Mukul Lokhande, Santosh Kumar Vishvakarma +1Efficient Neural Network Inference Energy-Efficient ML
Dept. of Electrical Engineering Indian Institute of Technology Indore Simrol 453552, Indore, India · EnICS Labs Bar-Ilan University Ramat Gan, Israel
EULER-ADAS: Energy-Efficient & SIMD-Unified Logarithmic-Posit Engine for Precision-Reconfigurable Approximate ADAS Acceleration
May 7, 2026Mukul Lokhande, Ratko Pilipovic, Omkar Kokane +2Energy-Efficient ML FPGA-Based Neural Network Acceleration
NSDCS Research Group, Dept. of Electrical Engineering, Indian Institute of Technology Indore, Indore, India · Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia · EnICS Labs, Bar-Ilan Univ., Ramat Gan 5290002, Israel
TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators
May 6, 2026Chang Meng, Hanyu Wang, Yuyang Ye +3AI Accelerator Inference Energy-Efficient ML
Eindhoven University of Technology Eindhoven, The Netherlands · University of California, Los Angeles Los Angeles, USA · Chinese University of Hong Kong Hong Kong, China +2
AxMoE: Characterizing the Impact of Approximate Multipliers on Mixture-of-Experts DNN Architectures
May 6, 2026Omkar B Shende, Marcello Traiola, Gayathri AnanthanarayananEfficient Neural Network Inference Edge Inference
Dept. of Computer Science and Engineering · Univ Rennes, CNRS, Inria, IRISA

Oct 5, 2026, cs.LGJ/K move · Enter open · S save

Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference

Nathaniel Kaye Mellor, Shreejith Shanker, George Floros

Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland

Edge Computing Graph Sparsification Convolutional Neural Networks FPGA-Based Neural Network Acceleration Approximate Computing

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Graph Convolutional Networks (GCNs) have emerged as a powerful framework for learning from graph-structured data, yet their deployment on resource-constrained edge platforms remains challenging due to the computational and memory demands of sparse graph aggregation. This work presents an FPGA-based GCN accelerator that combines DSpar graph sparsification, 8-bit quantization, and approximate multipliers on the AMD Kria KV260. Evaluated on Cora, LastFM Asia, and Amazon Photo, the design explores the interaction between sparsification and approximation across graphs with widely varying densities. Results show that the effectiveness of approximate arithmetic is governed by accumulation depth within GCN computations. Approximate multipliers are most effective when applied to sparse aggregation operations, while graph sparsification further improves their viability by reducing aggregation depth. The combined approach achieves up to 9.88

\times

speedup while maintaining 86.6% classification accuracy on Amazon Photo, and 1.52

\times

speedup with 77.0% accuracy on Cora, with total power consumption below 1 W. These results demonstrate that graph sparsification and approximate computing are complementary techniques whose co-optimization enables efficient low-power GCN inference on edge FPGA platforms.

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