arXiv AI September 28, 2026 By Mohd Moin Khan, Naman Srivastava, Pandarasamy Arjunan

ENAS: An Efficient Hardware-Aware Neural Architecture Search Framework for TinyML on Resource-Constrained Microcontrollers

ENAS is a hardware‑aware neural architecture search framework tailored for TinyML on microcontrollers. It uses a static feasibility check, a cell‑based search space with various block types and skip connections, and a three‑stage hybrid search strategy (random → top‑K → mutation) with cross‑run caching. The framework runs efficiently without GPUs, achieving significant search‑time speedups and competitive accuracy on Visual Wake Words and Melanoma Cancer benchmarks across a range of microcontrollers.

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benchmarks

arXiv Machine Learning

Jun 16

CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systems

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By Joseph Q. Zales, Pragya Sharma, Mani Srivastava

efficiency

arXiv Machine Learning

Jul 31

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By Jason Weitz, Dmitri Demler, Benjamin Hawks, Aaron Wang, Nhan Tran, Javier Duarte

agents fine-tuning efficiency

arXiv Machine Learning

Jun 5

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

arXiv:2605. 16138v2 Announce Type: replace Abstract: Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost.

By Jason Weitz, Dmitri Demler, Benjamin Hawks, Aaron Wang, Nhan Tran, Javier Duarte

agents fine-tuning efficiency

arXiv AI

Aug 10

Predictive Multi-Tier Memory Management for KV Cache in Large-Scale GPU Inference

arXiv:2604. 26968v2 Announce Type: replace-cross Abstract: Key-value (KV) cache memory management is the primary bottleneck limiting throughput and cost-efficiency in large-scale GPU inference serving.

By Sanjeev Rao Ganjihal

agents efficiency

arXiv Machine Learning

Aug 11

Gradient Under Microscope: Benchmarking Resource Utilization of Memory-Efficient Gradient Computation Methods

arXiv:2608. 08961v1 Announce Type: new Abstract: AI training's rising resource intensity is straining electricity supplies and carbon budgets, motivating systematic study of memory-efficient training on constrained hardware.

By Sarthak Mahapatra, Zihan Zhou, Khatoon Khedri, Mehdi Hosseinzadeh, Reza Rawassizadeh

llms computer-vision multimodal benchmarks

arXiv AI

Jun 10

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

arXiv:2606. 10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision.

By Tyler King, Timothee Leleu

llms

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