Research Paper Submission Categories
Artificial Intelligence (AI)
Artificial intelligence (AI) topic highlights advances in the field with a focus on design automation and designs at the cross section between machine learning (ML) and AI algorithms and hardware. While artificial intelligence and artificial neural network research has been ongoing for more than half a century, recent advances in accelerating the pace and scale of machine learning enabled by tensor-flow based gradient optimization in deeply layered convolutional networks (convnets) are revolutionizing the impact of artificial intelligence on every aspect of our daily lives, ranging from smart consumer electronics and services to self-navigating cars and personalized medicine. These advances in deep learning are fueled by computing architectures tailored to the distributed nature of learning and inference in neural networks, akin to the distributed nature of neural information processing and synaptic plasticity in the biological brain. Neuromorphic brain-inspired electronics for ML/AI aim at porting the brain's efficacy, efficiency, and resilience to noise and variability to electronic equivalents in standard CMOS and emerging technologies, offering new design challenges and opportunities to advance computing architecture beyond Moore's law scaling limits.
AI sessions at DAC will highlight the fundamentals, accomplishments to date, and challenges ahead in AI algorithms and system design, as well as design automation, providing a forum for researchers and engineers across all of the widely varying disciplines involved to connect, engage, and join in shaping the future of this exciting field.Artificial intelligence (AI) topic highlights the advances in the field with a focus on machine learning model development, AI inspired models of computation, security aspects related to AI/ML, and application of AI to autonomous systems. While artificial intelligence and artificial neural network research has been ongoing for more than half a century, recent advances in accelerating the pace and scale of machine learning (ML) and deep neural networks (DNNs) are revolutionizing the impact of artificial intelligence on every aspect of our daily lives, ranging from smart consumer electronics to personalized medicine and services.
The AI sessions at DAC focus on the fundamentals, accomplishments to date, and challenges ahead in models, algorithms, applications, and security/privacy issues, providing a forum for researchers and practitioners across all the widely varying disciplines involved to connect, engage, and join in shaping the future of this exciting field.
AI1. AI/ML Algorithms
AI1.1 Hardware-aware ML model development
AI1.2 Efficient ML training, inference, and serving
AI1.3 Approximation techniques for neural network training and inference
AI1.4 Testing, debugging, and monitoring of ML applications
AI1.5 Interpretability and explainability for ML models
AI2. AI/ML Application and Infrastructure
AI2.1 Application-driven Al/ML learning/models/inferences
AI2.2 Application-driven approximations in design for AI/ML
AI2.3 Infrastructures for AI (including datasets, implementations)
AI2.4 Interpretability and explainability for ML applications
AI3. AI/ML Architecture Design
AI3.1 AI/ML accelerator, processing engine design and architecture
AI3.2 Application-specific AI/ML architectures
AI3.3 Approximation techniques for hardware architecture
AI4. AI/ML System and Platform Design
AI4.1 Hardware/software codesign and co-optimization
AI4.2 Specialized AI/ML system design
AI4.3 Architecture-algorithm co-design for approximate computing
AI4.4 AI/ML system modeling and simulation methodologies
AI4.5 Evaluation and measurement of AI/ML systems
| back to top |
Design (DES)
DAC has served as a meeting place for designers of electronic systems and providers of electronic design automation tools for over five decades. Increasingly, the challenges faced by the industry require cross-domain interaction of researchers and practitioners working on electronic design (circuit, architecture, and embedded systems design) and researchers working on design methodologies and tools.
The design topics covered in the research track include the design of cyber-physical, SoC architectures, in-memory and near-memory computing architectures, AI/ML hardware and systems, digital and analog circuits, emerging device technologies, and quantum computing.
Separately the Engineering Tracks allows tool users to share challenges and benefits of different tools, flows, and methodologies. In addition, it provides excellent opportunities for education and networking between end users and tool developers. There is no other way to improve your “design IQ” in such a short amount of time than to attend the Engineering Tracks.
DES1. SoC, Heterogeneous, and Reconfigurable Architectures
DES1.1 Architectures for stochastic, statistical and approximate computing
DES1.2 SoC and heterogeneous multi- and many-core architectures
DES1.3 Run-time and design-time reconfigurable processor architectures
DES1.4 2.5D/3D heterogeneous integration of compute, memory and communication platforms
DES1.5 Fault-tolerant architectures
DES2A. In-memory and Near-memory Computing Circuits
DES2A.1 Circuit techniques for near- or in-memory processing
DES2A.2 Memory and storage technologies for near- or in-memory processing,
DES2A.3 Emerging technologies for in-memory and near-memory computing
DES2A.4 Circuit-Inspired architectures for in/near-memory computing
DES2B. In-memory and Near-memory Computing Architectures, Applications and Systems
DES2B.1 Near- or in-memory data management and processing models
DES2B.2 Architectures for near- or in-memory processing
DES2B.3 Memory and storage architectures for near- or in-memory processing
DES2B.4 Data reorganization engines for specific applications
DES2B.5 System/architecture interaction, execution model, interfaces
DES2B.6 Specialized architectures for key workloads taking advantage of near- and -in-memory processing
DES3. Emerging Models of Computation
DES3.1 Biologically-based or biologically-inspired computing systems
DES3.2 Design automation for system & synthetic biology
DES3.3 Neuromorphic and brain-inspired computing
DES3.4 Neuromorphic and brain-inspired processors
DES3.5 Neuromorphic and brain-inspired circuits
DES4. Digital and Analog Circuits
DES4.1 Novel circuits for emerging AI/ML workloads
DES4.2 Digital circuits and systems
DES4.3 Analog circuits and data converters
DES4.4 RF, wireless & wireline circuits and systems
DES4.5 Imagers, MEMS, medical, and display circuits
DES4.6 Circuits for memory design
DES4.7 Power management circuits
DES4.8 2.5-D and 3-D integrated circuit designs
DES4.9 Circuit design for secure computing
DES5. Emerging Device and Interconnect Technologies
DES5.1 New transistor structures
DES5.2 Beyond-CMOS devices (e.g., steep-slope devices, spintronics)
DES5.3 New process technologies (e.g. superconducting)
DES5.4 Nanotechnologies, nanowires, nanotubes
DES5.5 Emerging non-volatile memory devices
DES6. Quantum Computing
DES6.1 Quantum computing applications and algorithms
DES6.2 Quantum computing hardware architecture and design
DES6.3 Quantum computing technology
DES6.4 EDA for quantum computing systems
| back to top |
Electronic Design Automation (EDA)
EDA (Electronics Design Automation) is becoming ever more important with the continuous scaling of semiconductor devices and the growing complexities of their use in circuits and systems. Demands for lower-power, higher-reliability and more agile electronic systems raise new challenges to both design and design automation of such systems. For the past five decades, the primary focus of research track at DAC has been to showcase leading-edge research and practice in tools and methodologies for the design of circuits and systems.
In addition to the traditional EDA topics ranges from physical design to system architectures, DAC features high-quality papers on design research, design practices, and design automation for cross-cutting topics including low-power, reliability, multicore/application specific/heterogeneous architectures, 3-D integrations, emerging device technologies, design automation of “things”, and their applications. The track also highlights the advances of AI/ML techniques in the field of design automation. DAC’s EDA technical program has been ensuring the best-in-class solutions that promise to advance EDA.
EDA1. Design Methodologies for System-on-Chip and 3D/2.5D System-in Package
EDA1.1 3D/2.5D SoC/package and communication technologies
EDA1.2 System-on-Chip (SoC) specification, modeling, analysis, simulation, and verification
EDA1.3 Application-specific processor design tools
EDA1.4 Design tools for accelerator-rich architectures and heterogeneous multi-cores
EDA1.5 Tools for reconfigurable computing
EDA1.6 HW/SW co-design, interface synthesis, and co-verification
EDA1.7 System-level methods for reliability and aging
EDA1.8 In-Package and On-Chip Communication architecture modeling and analysis
EDA1.9 Synthesis and optimization of communication architectures
EDA1.10 NoC architectures and design methodologies
EDA1.11 Communication architectures using alternative technologies (e.g., nanophotonics, RF)
EDA2. Design Verification and Validation
EDA2.1 Functional and transaction-level modeling and validation, coverage and test generation for hardware and embedded systems
EDA2.2 Emulation and hardware acceleration
EDA2.3 Formal and semi-formal verification and verification technologies
EDA2.4 Verification of firmware, software, and hybrid hardware/software systems
EDA2.5 Machine learning techniques for verification
EDA2.6 Validation of cognitive systems
EDA2.7 Verification on the cloud
EDA3. Timing Analysis and Optimization
EDA3.1 Timing analysis and simulation/delay modeling
EDA3.2 Signal integrity and noise analysis
EDA3.3 Process technology modeling for timing analysis
EDA3.4 Timing analysis and optimization for 3D/2.5D
EDA4. Power Analysis and Optimization
EDA4.1 System-level low-power design analysis and management
EDA4.2 Architectural power reduction techniques and analysis tools
EDA4.3 Low-power circuit design methods and tools
EDA4.4 Thermal analysis and management
EDA4.5 Power analysis related process technology modeling
EDA4.6 Power and thermal analysis and optimization for 3D/2.5D
EDA5 . RTL/Logic Level and High-level Synthesis
EDA5.1 Combinational, sequential and asynchronous logic synthesis
EDA5.2 Technology mapping, cell-based design and optimization
EDA5.3 High-level, behavioral, algorithmic, and architectural synthesis, “C” to gates tools and methods
EDA5.4 Synthesis for FPGAs
EDA5.5 Synthesis for circuits in emerging device technologies
EDA5.6 Synthesis on the cloud
EDA6. Analog CAD, Simulation, Verification and Test
EDA6.1 Analog, mixed-signal, and RF design methodologies
EDA6.2 Automated synthesis, place and route, and optimization of analog designs
EDA6.3 Analog, mixed-signal, RF, electromagnetic, substrate noise modeling and simulation
EDA6.4 Model order reduction techniques for analog/RF designs
EDA7. Physical Design and Verification
EDA7.1 Floorplanning, partitioning, placement, and routing
EDA7.2 Interconnect and clock network planning and synthesis
EDA7.3 Cross-layer placement and routing optimization for timing/power/yield
EDA7.4 Physical design of 3D/2.5D IC and package (e.g., TSV, interposer, monolithic)
EDA7.5 Layout optimization for optical interconnects
EDA7.6 Layout verification
EDA7.7 Physical design on the cloud
EDA7.8 Physical Design for secure computing
EDA8. Design for Manufacturability and Reliability
EDA8.1 Design-technology co-optimization (DTCO)
EDA8.2 Standard and custom cell design and optimization
EDA8.3 Process technology characterization, extraction, and modeling
EDA8.4 Reticle enhancement, lithography-related design optimizations and design rule checking
EDA8.5 Design for manufacturability, yield, defect tolerance, cost issues, and DFM impact
EDA8.6 Device-, gate, and circuit-level techniques for reliability analysis and optimization (e.g., soft error, aging, etc.)
EDA8.7 3D/2.5D manufacturability and reliability, including mechanical stress
EDA8.8 Post-Layout optimizations
EDA9. Test, Validation and Silicon Lifecycle Management
EDA9.1 Fault modeling, ATPG, DFT, BIST, compression
EDA9.2 Memory, FPGA, and emerging technology test and reliability
EDA9.3 SoC, board- and system-level test
EDA9.4 Post-silicon test, optimization, and defect diagnosis
EDA9.5 Post-silicon validation and debug
EDA9.6 Test for analog/mixed-signal/RF circuits
EDA9.7 Silicon lifecycle management and system sustainability
EDA9.8 Silicon health monitoring and predictive maintenance
| back to top |
Security (SEC)
Security topic at DAC focuses on the urgent need to create, analyze, evaluate, and improve the hardware, embedded systems and software base of the contemporary security solutions. Secure and trustworthy software and hardware components, platforms and supply chains are vital to all domains including financial, healthcare, transportation, and energy. Security of systems is becoming equally important. A revolution is underway in many industries that are "connecting the unconnected". Such cyber physical systems, e.g., automobiles, smart grid, medical devices, etc., are taking advantage of integration of physical systems with the information systems. Notwithstanding the numerous benefits, these systems are appealing targets of attacks. Attacks on the cyber-part of such systems can have disastrous consequences in the physical world. The scope and variety of attacks on these systems present design challenges that span embedded hardware, software, networking, and system design.
Security sessions at DAC will feature invited special sessions, panels, and lecture/poster presentations by both engineers and researchers to share their knowledge and experience on this evolving environment.
SEC1. AI/ML Security/Privacy
- SEC1.1 Privacy and security for AI/ML applications
- SEC1.2 AI/ML-based attacks and defenses
- SEC1.3 Adversarial machine learning attacks and defenses
- SEC1.4 AI/ML for cyber defense
- SEC1.5 Fairness for AI/ML applications
SEC2. Hardware Security: Primitives, Architecture, Design & Test
- SEC2.1 Hardware security primitives for cryptography, key generation, and authentication
- SEC2.2 Trusted IP and system-on-chip (SoC) design and manufacturing
- SEC2.3 Emerging technologies (Nanoscale devices, 3D, etc.) and security
- SEC2.4 Hardware security verification, validation and test
- SEC2.5 Post-quantum crypto algorithms and implementations
- SEC2.6: Design automation for security and privacy preserving
SEC3. Hardware Security: Attack and Defense
- SEC3.1 Hardware-enabled side-channel attacks and defenses
- SEC3.2 Hardware supply chain protection and anti-counterfeiting
- SEC3.3 Reverse engineering and hardware obfuscation
- SEC3.4 AI/ML-based hardware attacks and defenses
SEC4. Embedded and Cross-Layer Security
- SEC4.1 Embedded architecture, software and system-level techniques for security and privacy
- SEC4.2 Cyber-physical systems, IoT and edge security
- SEC4.3 Embedded security: metrics, models, verification and validation
- SEC4.4 Cloud security
- SEC4.5 Software-driven side-channel attacks and defenses
- SEC4.6 Privacy preserving computing
Systems (SYS)
System design is the art of choosing and designing the proper combination of hardware and software components to achieve system level design goals like speed, efficiency, reliability, security, and safety. Systems are an increasingly diverse, disruptive, and challenging field for designs ranging from mobile devices, medical devices, automotive, robotics, drones, industrial and beyond. Embedded software is built into devices that may not necessarily be recognized as computing devices (e.g., thermostats, toys, defibrillators, and anti-lock brakes), but nevertheless controls the functionality and perceived quality of these devices.
The SYS sessions at DAC provide a forum for discussing the challenges of embedded design and an opportunity for leaders in the industry and academia to come together to exchange ideas and roadmaps for the future for this rapidly expanding area.
SYS1. Autonomous Systems (Automotive, Robotics, Drones)
SYS1.1 Autonomous Systems Design Tools and Methodologies
SYS1.2 Autonomous Systems Architectures
SYS1.3 Autonomous Systems Safety and Reliability
SYS2. Design of Cyber-Physical Systems and IoT
SYS2.1 Cyber-physical systems and Internet-of-Things (IoT) platforms
SYS2.2 Low-power and energy-efficient design techniques for IoT
SYS2.3 Partitioned Edge/hub/cloud processing
SYS2.4 Dependable and safety-critical embedded system design
SYS2.5 Networking and storage system design
SYS2.6 Advanced wireless communication system design
SYS3. Embedded Software
SYS3.1 Embedded software verification methodologies
SYS3.2 Embedded operating systems, middleware, runtime support, resource management, and virtual machines
SYS3.3 Software techniques for multicores, GPUs, and multithreaded embedded architectures
SYS3.4 Compilation strategies, code transformation and parallelization techniques for embedded systems
SYS3.5 Domain-specific embedded libraries (e.g., for machine learning)
SYS3.6 Embedded Software Development Case Studies (e.g., ANDROID development, ARM-based systems, RISC-V based systems etc.)
SYS4. Embedded System Design Tools and Methodologies
SYS4.1 Embedded system specification, virtual prototyping and simulation
SYS4.2 Embedded system synthesis and optimization
SYS4.3 Analysis of embedded system QoS metrics - performance, battery life, reliability, etc.
SYS4.4 Design methodologies for self-aware, self-adaptive and autonomous embedded systems
SYS4.5 Design methodology for mobile, wearable and Internet of Things devices
SYS5. Embedded Memory and Storage Systems
SYS5.1 On-chip memory architectures and management: Scratchpads, compiler controlled memories, etc.
SYS5.2 Embedded storage systems organization and management
ESS3.3 Memory and Storage hierarchies with emerging memory technologies
SYS6. Time-Critical and Fault Tolerant System Design
SYS6.1 Real-time analysis and tool flows
SYS6.2 WCET methods and tools for embedded hardware/software systems
SYS6.3 Mixed-Criticality system design
SYS6.4 Fault-tolerant embedded system design
| back to top |