On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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5 results for “System-on-Chip”

Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learningcs.LG

The paper proposes a method to estimate synthesis flow quality across different technologies and designs.

problem Challenges in developing high-quality synthesis flows for ICs and SoCs.

method Training a Long Short-Term Memory (LSTM) based RNN regressor to predict Quality-of-Result (QoR) for unseen synthesis flows.

result The approach achieves over 98% accuracy in predicting QoRs within one technology and over 96.3% accuracy across different technologies and designs.

Synthesis flows Quality-of-Result Recurrent Neural Networks Transfer Learning Integrated Circuits

2018-11-14abs ↗pdf ↗

SIMCom: Statistical Sniffing of Inter-Module Communications for Run-time Hardware Trojan Detectioncs.CR

SIMCom detects HTs by analyzing communication patterns in SoCs.

problem Timely detection of Hardware Trojans in secure integrated circuits.

method Multi-parameter statistical modeling of communication channels in SoCs.

result SIMCom detects HTs with less than 1% area and power overhead.

Hardware Trojans System-on-Chip Run-time Detection Statistical Modeling Side-Channel Analysis

2018-11-04abs ↗pdf ↗

Neural Network Inference on Mobile SoCscs.LG

Mobile SoCs can improve ML inference by 2x when all components are used concurrently.

problem Power-performance trade-offs in mobile ML inference on heterogeneous SoCs.

method Quantitative evaluation of different ML components on mobile SoCs.

result Mobile SoCs can achieve up to 2x improvement in inference performance by using all components concurrently.

Mobile SoCs Machine Learning Inference Heterogeneous Computing Power-Performance

2019-08-24abs ↗pdf ↗

Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detectioneess.SP

Improved cardiac arrhythmia detection in wearable devices with neural networks.

problem Resource constraints in low-power wearable devices for accurate arrhythmia detection.

method Adapted a convolutional-recurrent neural network to a low-power microcontroller, optimizing for precision and memory usage.

result Reduced

F_1

score from 0.8 to 0.784 in fixed-point precision, with a 195.6KB memory footprint and 33.98MOps/s throughput.

cardiac arrhythmia wearable devices neural networks low-power machine learning

2020-01-08abs ↗pdf ↗

Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devicescs.DC

MetaDVFS uses device and application metadata to improve DVFS efficiency.

problem Improving energy efficiency in mobile platforms with diverse applications and hardware.

method Formulates DVFS as a multi-task reinforcement learning problem and introduces MetaDVFS, leveraging metadata for knowledge transfer.

result MetaDVFS achieves up to 26% improvement in Quality of Experience and up to 17% improvement in Performance-Power Ratio.

Dynamic Voltage and Frequency Scaling Reinforcement Learning Metadata Heterogeneous Devices Mobile Platforms

2025-09-23abs ↗pdf ↗