About the Journal

 Aim

The aim of Progress in AI-Accelerated VLSI Systems, ISSN 3139-2148, is to explore and present cutting-edge research advancements where Artificial Intelligence (AI) enhances the design, optimization, verification, and operation of VLSI systems. It focuses on the integration of AI-driven methodologies to improve performance, power efficiency, area optimization, reliability, and time-to-market of modern semiconductor and hardware architectures.

 Scope

This topic covers theoretical and practical developments related to AI-enabled VLSI design and hardware acceleration, including but not limited to:

1. AI in VLSI Design Automation

  • Machine learning for EDA workflows

  • AI-based synthesis, placement, and routing

  • Predictive modeling for timing, power, and area

  • Reinforcement learning for design-space exploration

2. AI-Driven Verification and Testing

  • ML-assisted functional verification

  • Automated bug detection and debugging

  • AI-based test pattern generation

  • Fault prediction and reliability improvement

3. Hardware Architectures for AI Acceleration

  • AI accelerator chips (TPU/NPU-like architectures)

  • CNN/RNN/Transformer hardware implementations

  • Systolic arrays and deep learning processors

  • Energy-efficient compute engines

4. Low-Power and High-Performance VLSI using AI

  • AI-based power optimization methods

  • DVFS and adaptive power management

  • Thermal-aware design and prediction

  • Performance tuning using ML models

5. Edge AI and Embedded VLSI Systems

  • TinyML hardware design

  • AI acceleration for IoT and wearable devices

  • Neuromorphic and brain-inspired computing

  • Real-time inference on resource-limited platforms

6. Emerging Technologies and Future Trends

  • AI-assisted analog and mixed-signal design

  • 3D ICs, chiplets, and heterogeneous integration

  • Photonic and quantum-inspired VLSI acceleration

  • Secure and trustworthy AI hardware



Frequency of publication - Three issue per year 
Language - English
Subject - Engineering
Year of Starting - 2026
format of publication  - Online Only
ISSN - 3139-2148