2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)

2023年IEEE/ACM计算机辅助设计国际会议（ICCAD）

Important Dates:

Paper Submission Deadline:May 15, 2023

Notification of Acceptance:July 21, 2023

Date of the meeting:October 28 - November 2, 2023

Location: San Francisco, California, USA

Conference introduction:

Jointly sponsored by IEEE and ACM, ICCAD is the premier forum to explore new challenges, present leading-edge innovative solutions, and identify emerging technologies in the electronic design automation research areas. ICCAD covers the full range of CAD topics – from device and circuit level up through system level, as well as post-CMOS design. ICCAD has a long-standing tradition of producing cutting-edge, innovative technical program for attendees.

 Organizing Committee:

Call for Papers:

Original technical submissions on, but not limited to, the following topics are invited:

1.1 System Design

System-level specification, modeling, simulation, design flaws

System-level issues for 3D integration

System-level design case studies and applications

HW/SW co-design co-simulation, co-optimization, and co-explorationplatforms for emulation and rapid prototyping

Micro-architectural transformation

Multi-/many-core processor, GPL and heterogeneous SoC

Memory and storage architecture and system synthesis

System communication architecture, Network-on-chip design

Modelinqsimulation high-level synthesis power/performanceanalysis, programming of heterogeneous computing platforms

Application driven system design for big data

Analysis and optimization of data centers

1.2 Embedded, cyber-Physical (CPS), loT Systems

and S oftware

Al and machine learning for embedded systems

HW/SW codesign for embedded systems

Compute memory,storage interconnect for embedded systems

Domain-specific accelerators

Energy/power management and energy harvesting

Real-time software and systems

Middleware virtual machines, and runtime support

Dependable safe secure trustworthy embedded svstems

Embedded software: compilation, optimization, testing

CAD for loTedge and fog computing

Modelinganalysis verification of CPS systems

Green computing (smart grid, energy, solar panels, etc.)

CAD for application domains including wearables, health careautonomous systems smart cities

1.3 Tools and Design Methods with and for Artificia!Intelligence(AD

Compilers for deep neural networks

Design method for learning on a chip

Deep neural network for EDA

Tools and design methodologies for edge Al and TinyML

Performance analysis and modeling for Al accelerators

Reliability analysis for neural network designs

Contact Us:

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