Image de couverture de livre

Co-Design of CNN Accelerators for TinyML using Approximate Matrix Decomposition

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Morales, José Juan Hernández, Mentzos, Georgios, Hannig, Frank, Balaskas, Konstantinos, Zervakis, Georgios, Henkel, Jörg, Teich, Jürgen
Format:	Preprint
Publié:	2026
Sujets:	Hardware Architecture
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Sequential Printed MLP Circuits for Super TinyML Multi-Sensory Applications
par: Saglam, Gurol, et autres
Publié: (2024)

Late Breaking Results: Leveraging Approximate Computing for Carbon-Aware DNN Accelerators
par: Panteleaki, Aikaterini Maria, et autres
Publié: (2025)

Variability-Aware Approximate Circuit Synthesis via Genetic Optimization
par: Balaskas, Konstantinos, et autres
Publié: (2023)

Energy-Aware Heterogeneous Federated Learning via Approximate DNN Accelerators
par: Pfeiffer, Kilian, et autres
Publié: (2024)

Design and Optimization of Mixed-Kernel Mixed-Signal SVMs for Flexible Electronics
par: Afentaki, Florentia, et autres
Publié: (2025)

Support Vector Machines Classification on Bendable RISC-V
par: Vergos, Polykarpos, et autres
Publié: (2025)

Loop Control Management in Tightly Coupled Processor Arrays (TCPAs)
par: Walter, Dominik, et autres
Publié: (2026)

Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainability
par: Panteleaki, Aikaterini Maria, et autres
Publié: (2025)

TransAxx: Efficient Transformers with Approximate Computing
par: Danopoulos, Dimitrios, et autres
Publié: (2024)

Computing with Printed and Flexible Electronics
par: Tahoori, Mehdi B., et autres
Publié: (2025)

Hardware/Software Co-Design of RISC-V Extensions for Accelerating Sparse DNNs on FPGAs
par: Sabih, Muhammad, et autres
Publié: (2025)

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference
par: Woodward, Kieran, et autres
Publié: (2025)

Compact Yet Highly Accurate Printed Classifiers Using Sequential Support Vector Machine Circuits
par: Sertaridis, Ilias, et autres
Publié: (2025)

Symbolic Polyhedral-Based Energy Analysis for Nested Loop Programs
par: Nirmala, Avinash Mahesh, et autres
Publié: (2026)

Leveraging Highly Approximated Multipliers in DNN Inference
par: Zervakis, Georgios, et autres
Publié: (2024)

Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language Models
par: Vahdatpour, Mohammad Saleh, et autres
Publié: (2026)

Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow
par: Wiese, Philip, et autres
Publié: (2024)

Mapping and Execution of Nested Loops on Processor Arrays: CGRAs vs. TCPAs
par: Walter, Dominik, et autres
Publié: (2025)

Evaluation of CGRA Toolchains
par: Walter, Dominik, et autres
Publié: (2025)

Synthesis of Resource-Efficient Superconducting Circuits with Clock-Free Alternating Logic
par: Volk, Jennifer, et autres
Publié: (2024)

Evolutionary Approximation of Ternary Neurons for On-sensor Printed Neural Networks
par: Mrazek, Vojtech, et autres
Publié: (2024)

Accelerating TinyML Inference on Microcontrollers through Approximate Kernels
par: Armeniakos, Giorgos, et autres
Publié: (2024)

Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity
par: Somathilaka, Samitha, et autres
Publié: (2024)

Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables
par: Vergos, Theofanis, et autres
Publié: (2025)

Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables
par: Afentaki, Florentia, et autres
Publié: (2025)

Function Approximation Using Analog Building Blocks in Flexible Electronics
par: Duarte, Paula Carolina Lozano, et autres
Publié: (2025)

RISC-V Based TinyML Accelerator for Depthwise Separable Convolutions in Edge AI
par: Yildirim, Muhammed, et autres
Publié: (2025)

Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons
par: Afentaki, Florentia, et autres
Publié: (2023)

Design and In-training Optimization of Binary Search ADC for Flexible Classifiers
par: Duarte, Paula Carolina Lozano, et autres
Publié: (2024)

FERMI-ML: A Flexible and Resource-Efficient Memory-In-Situ SRAM Macro for TinyML acceleration
par: Lokhande, Mukul, et autres
Publié: (2025)

MAx-DNN: Multi-Level Arithmetic Approximation for Energy-Efficient DNN Hardware Accelerators
par: Leon, Vasileios, et autres
Publié: (2025)

eBPF-mm: Userspace-guided memory management in Linux with eBPF
par: Mores, Konstantinos, et autres
Publié: (2024)

Embedding Hardware Approximations in Discrete Genetic-based Training for Printed MLPs
par: Afentaki, Florentia, et autres
Publié: (2024)

SAMIPS: A Synthesised Asynchronous Processor
par: Zhang, Qianyi, et autres
Publié: (2024)

Design-Oriented Modeling of TSV Substrate Noise Coupling to Ring VCOs
par: Exouzidis, Ilias, et autres
Publié: (2026)

DSLR-CNN: Efficient CNN Acceleration using Digit-Serial Left-to-Right Arithmetic
par: Nisar, Malik Zohaib, et autres
Publié: (2025)

PIMSYN: Synthesizing Processing-in-memory CNN Accelerators
par: Li, Wanqian, et autres
Publié: (2024)

An Architectural Error Metric for CNN-Oriented Approximate Multipliers
par: Liu, Ao, et autres
Publié: (2024)

Design and Analysis of Approximate Hardware Accelerators for VVC Intra Angular Prediction
par: de Fraga, Lucas M. Leipnitz, et autres
Publié: (2025)

ARMOR: Robust and Efficient CNN-Based SAR ATR through Model-Hardware Co-Design
par: Wickramasinghe, Sachini, et autres
Publié: (2026)