Deep neural networks have witnessed remarkable advancements in recent years and have become integral to various applications. However, alongside these developments, training and deployment of neural network models on embedding and edge devices face significant challenges due to limited memory and computational resources.
arXiv:2607. 28418v1 Announce Type: cross Abstract: Pruning is a promising approach for improving the efficiency of LLMs.
By Haozhe Hu, Hao Wu, Peiran Yin, Chao Han, Yunpu Ma, Xiaoyu Shen
arXiv:2608. 05499v1 Announce Type: cross Abstract: Modern deep neural networks achieve strong performance, but their scale makes them costly and slow, especially on resource-constrained edge devices.
By Sadegh Jafari, Mohiuddin Bilwal, Fan Zhou, Brian Gelder, Ali Jannesari
Modern deep neural networks achieve strong performance, but their scale makes them costly and slow, especially on resource-constrained edge devices. Pruning and quantization address this, but rely on manual, expert choices and on algorithms that are hard to apply across architectures.
FAME is an FPGA-based platform that evaluates approximate multipliers directly in hardware, eliminating slow CPU/GPU LUT emulation and reducing evaluation time for DNN inference. It also introduces a pattern-guided retraining method that uses multiplier-specific patterns to recover accuracy losses. Experiments on ResNet‑18 and MobileNetV2 over ImageNet show up to 3.47× faster multiplier evaluation and a 65.5% accuracy improvement over prior retraining approaches.
By Rappy Saha, Nima Amirafshar, Jude Haris, Nima Taherinejad, Jos\'e Cano
Deep Microcompression (DMC) is a hardware‑aware pipeline that combines structured pruning, quantization‑aware training, and fixed‑length bit‑packing to enable deep learning inference on bare‑metal microcontrollers. The method achieves a 55.8× weight compression on LeNet‑5 while maintaining 98.77% accuracy, and produces a dependency‑free C library with deterministic latency. On the RP2040 Cortex‑M0+ microcontroller, DMC cuts binary size threefold compared to TensorFlow Lite while matching its accuracy, and it is the first documented deployment of a standard CNN on the 2 KB SRAM ATmega328P.
By Opegbemi Matthias Busoye, Tolulope Matthew Busoye, Eghonghon-aye Eigbe