Program for 2018 31st IEEE International System-on-Chip Conference (SOCC)
Tuesday, September 4
Tuesday, September 4 9:00 - 10:30
Tuesday, September 4 10:30 - 10:50
Tuesday, September 4 10:50 - 12:20
Tutorial 1C: IoT Security: - Threats, Security Challenges and IoT Security Research and Technology Trends
Tuesday, September 4 12:20 - 13:40
Tuesday, September 4 13:40 - 15:10
Tuesday, September 4 15:10 - 15:30
Tuesday, September 4 15:30 - 17:00
Wednesday, September 5
Wednesday, September 5 9:00 - 9:10
Wednesday, September 5 9:10 - 9:20
Technical Program Overview
Wednesday, September 5 9:20 - 10:10
Organizations today looking to accelerate their AI and deep learning applications must make significant compromises in performance, price and ease of use due to the limited capabilities of traditional hardware solutions, which only address a narrow set of requirements for very specific needs. For example, they have to use multiple solutions for a single AI application, or they need to use different implementations for different types of data sets. This has created "silos" of different hardware architectures, making the extraction of intelligence from data difficult to manage, provision and scale to meet the hyper growth of AI applications.
In this presentation, Wave Computing VP of Marketing Fadi Azhari will explain how the lack of a common AI platform, from the datacenter to the edge, is slowing AI market growth and reducing the productivity of data scientists in a wide range of fields. He will also explain how Wave Computing is addressing this challenge, and enabling data scientists to experiment, develop, test and deploy neural networks on a common platform spanning to the Edge of Cloud - and enabling enterprise-class companies better leverage AI as a fundamental part of their digital strategies.
Wednesday, September 5 10:10 - 10:30
Wednesday, September 5 10:30 - 12:10
W1A: Low Power Design
- 10:30 On a New Hardware Trojan Attack on Power Budgeting of Many Core Systems
- 10:55 Holistic Energy Management with µProcessor Co-Optimization in Fully Integrated Battery-less IoTs
- 11:20 Cloud Motion Vector Estimation Using Scalable Wireless Sensor Networks
- 11:45 A Discontinuous Charging Technique with Programmable Duty-Cycle for Switched-Capacitor Based Energy Harvesting Circuits in IoT Applications
W1B: RF Circuits
- 10:30 An FSK Transceiver for USB Power Delivery in 0.14-Um CMOS Technology
- 10:55 A 64 dB Dynamic Range Programmable Gain Amplifier for Dual Band WLAN 802.11Abg IF Receiver in 0.18 Um CMOS Technology
- 11:20 Design and Analysis of 66GHz Voltage Controlled Oscillators for FMCW Radar Applications with Phase Noise Impact Consideration
Wednesday, September 5 12:10 - 13:30
Wednesday, September 5 13:30 - 14:20
Compute-in-Memory (CiM) techniques focus on reducing data movement by integrating compute elements within or near the memory primitives. While there have been decades of research on various aspects of such logic and memory integration, the confluence of new technology changes and emerging workloads makes us revisit this design space. This talk focuses on new functionality embedded with SRAMs using emerging monolithic 3D integration. Properties of the new technology transform the costs of embedding such new functionality compared to prior efforts. This work also explores how compute functionality can be embedded into cross-point style non-volatile memory systems. The talk will provide insights into the benefits provided by the proposed technique and evaluate the energy efficiency of the proposed designs.
Wednesday, September 5 14:20 - 14:40
Wednesday, September 5 14:40 - 16:20
W2A: Emerging and Evolutionary Design
- 14:40 A Twin Memristor Synapse for Spike Timing Dependent Learning in Neuromorphic Systems
- 15:05 A Low-Power Arithmetic Element for Multi-Base Logarithmic Computation on Deep Neural Networks
- 15:30 A Scalable High-Precision and High-Throughput Architecture for Emulation of Quantum Algorithms
- 15:55 Taxonomy of Spatial Parallelism on FPGAs for Massively Parallel Applications
W2B: High Performance Low Power Circuits
- 14:40 An Output-Capacitorless Adaptively Biased Low-Dropout Regulator with Maximum 132-MHz UGF and Without Minimum Loading Requirement
- 15:05 An Ultra-Low-Voltage Sub-threshold Pseudo-Differential CMOS Schmitt Trigger
- 15:30 Reconfigurable Clock Generator with Wide Frequency Range and Single-Cycle Phase and Frequency Switching
- 15:55 A New Circuit Topology for High-Performance Pulsed Time-of-Flight Laser Radar Receivers
Wednesday, September 5 16:20 - 18:00
- 16:20 Policy-Based Security Modelling and Enforcement Approach for Emerging Embedded Architectures
- 16:45 An Entropy Analysis Based Intrusion Detection System for Controller Area Network in Vehicles
- 17:10 A High-performance VLSI Architecture of the PRESENT Cipher and Its Implementations for SoCs
- 17:35 Leakage Power Analysis (LPA) Attack in Breakdown Mode and Countermeasure
W3B: Reconfigurable Architectures and Applications
- 16:20 Optimized Counter-Based Multi-Ported Memory Architectures for Next-Generation FPGAs
- 16:45 Performance Modeling of VIA-switch FPGA for Device-Circuit-Architecture Co-Optimization
- 17:10 A Content-Adapted FPGA Memory Architecture with Pattern Recognition Capability and Interval Compressing Technique
- 17:35 An ASIC Design of Multi-Electrode Digital Basket Catheter Systems with Reconfigurable Compressed Sampling
Wednesday, September 5 18:00 - 20:00
WP: Poster Session & Reception
- A Multi-Objective Architecture Optimization Method for Application-Specific NoC Design
- Building an Acceleration Overlay for Novice Students
- Pro-Active Policing and Policy Enforcement Architecture for Securing MPSoCs
- Compact Modeling and Design of Magneto-electric Transistor Devices and Circuits
- Flexible Self-Healing Router for Reliable and High-Performance Network-on-Chips Architecture
- Hardware Acceleration of HDR-Image Tone Mapping on an FPGA-CPU Platform Through High-Level Synthesis
- PAT-NOXIM: A Precise Power & Thermal Cycle-Accurate NoC Simulator
- PCNNA: A Photonic Convolutional Neural Network Accelerator
- Data Readout Triggering for Phase 2 of the Belle II Particle Detector Experiment Based on Neural Networks
- Towards Designing Optimized Low Power Reversible Demultiplexer for Emerging Nanocircuits
- Noise Aware Power Adaptive Partitioned Deep Networks for Mobile Visual Assist Platforms
- An Automated Fault Injection Platform for Fault Tolerant FFT Implemented in SRAM-Based FPGA
- A Quantitative Approach to SoC Functional Safety Analysis
- A 81nW Error Amplifier Design for Ultra Low Leakage Retention Mode Operation of 4Mb SRAM Array in 40Nm LSTP Technology
- A Practical Sense Amplifier Design for Memristive Crossbar Circuits (PUF)
Thursday, September 6
Thursday, September 6 9:00 - 9:50
Due to the end of the Moore's law in clocking and Dennard's scaling, we are reaching very crippling limits with our current von Neumann processor paradigms. All the help is sought from both technology and architectures to innovate and engender new processing paradigms that can overcome those limitations and define the future of computing. New ideas and directions ranged from neuromorphic processors, to analog, memristors, quantum and the use of nano photonics. This talk will examine a number of these emerging directions and work by the community including ours and evaluate some of the associated implications for the future of computing.
Tarek El-Ghazawi is a Professor in the Department of Electrical and Computer Engineering Opens in a new window at The George Washington University Opens in a new window, where he leads the university-wide Strategic Academic Program in High-Performance Computing Opens in a new window. He is the founding director of The GW Institute for Massively Parallel Applications and Computing Technologies (IMPACT) Opens in a new window and a founding Co-Director of the NSF Industry/University Center for High-Performance Reconfigurable Computing (CHREC) Opens in a new window. El-Ghazawi's research interests include high-performance computing, computer architectures, reconfigurable, embedded computing and computer vision. He is one of the principal co-authors of the UPC parallel programming language and the first author of the UPC book from John Wiley and Sons. He has received his Ph.D. degree in Electrical and Computer Engineering from New Mexico State University in 1988. El-Ghazawi has published close to 250 refereed research publications in this area.
Thursday, September 6 9:50 - 10:40
Artificial neural networks, which dominate artificial intelligence applications such as object recognition and speech recognition, are in evolution. To apply neural networks to wider applications, customized hardware are necessary since CPU and GPU are not efficient enough. Numerous architectures are proposed in the past 4 years to boost the energy efficiency of deep learning inference processing, including Tsinghua and Deephi's effort. In this talk, we will talk about different architectures based on CMOS technologies, including 200GOPS/W FPGA accelerators, about 1-5TOPS/W chips with DDR subsystems, and over 50TOPs/W chips with everything on chip. The possibilities and trends of adopting emerging NVM technology for efficient learning systems, i.e., in-memory-computing, will also be discussed as one of the most promising ways to improve the energy efficiency.
Yu Wang received his B.S. degree in 2002 and Ph.D. degree (with honor) in 2007 from Tsinghua University, Beijing. He is currently a Tenured Associate Professor with the Department of Electronic Engineering, Tsinghua University. His research interests include brain inspired computing, application specific hardware computing, parallel circuit analysis, and power/reliability aware system design methodology. Dr. Wang has authored and coauthored over 200 papers in refereed journals and conferences. He has received Best Paper Award in FPGA 2017, NVMSA17, ISVLSI 2012, and Best Poster Award in HEART 2012 with 9 Best Paper Nominations. He is a recipient of DAC Under-40 Innovator Award in 2018 and IBM X10 Faculty Award in 2010. He served as TPC chair for ISVLSI 2018, ICFPT 2011 and Finance Chair of ISLPED 2012-2016, and served as program committee member for leading conferences in these areas, including top EDA conferences such as DAC, DATE, ICCAD, ASP-DAC, and top FPGA conferences such as FPGA and FPT. Currently he serves as Co-EIC for SIGDA E-Newsletter, Associate Editor for IEEE Trans on CAS for Video Technology, IEEE Transactions on CAD, and Journal of Circuits, Systems, and Computers. He also serves as guest editor for Integration, the VLSI Journal and IEEE Transactions on Multi-Scale Computing Systems. He is a recipient of NSF China Excellent Young Scholar, and is now serving as ACM distinguished speaker. He is an IEEE/ACM senior member. He is the co-founder of Deephi Tech (valued over 150M USD), which is a leading deep learning processing platform provider.
Thursday, September 6 10:40 - 11:00
Thursday, September 6 11:00 - 12:15
T1A: Memory Circuits and Applications
- 11:00 Reducing Memory Interference Latency of Safety-Critical Applications via Memory Request Throttling and Linux Cgroup
- 11:25 Memory Access Driven Memory Layout and Block Replacement Techniques for Compressed Deep Neural Networks
- 11:50 A Learning-guided Hierarchical Approach for Biomedical Image Segmentation
T1B: Digital and Mixed-Signal Circuits
Thursday, September 6 12:15 - 13:30
Thursday, September 6 13:30 - 14:20
Prognostic diagnosis is desirable for commercial core router systems to ensure early failure prediction and fast error recovery. The effectiveness of prognostic diagnosis depends on whether anomalies can be accurately detected before a failure occurs. However, traditional anomaly detection techniques fail to detect "outliers" when the statistical properties of the monitored data change significantly with time. This talk will describe recent advances in using time-series data analysis to detect anomalies through the real-time monitoring of key performance indicators in core routers. The speaker will describe the design of a changepoint-based anomaly detector ad health-status analyzer that first detects changepoints from collected time-series data, and then utilizes these changepoints to detect anomalies. A clustering method is first used to identify a wide range of normal/abnormal patterns from changepoint windows. Symbolic aggregation approximation and moving-average-based trend approximation are utilized to encode complex time series. Hierarchical agglomerative clustering and sequitur rule discovery are then to learn important global and local patterns. A comprehensive set of experimental results will be presented for data collected during 30 days of field operation from over 20 core routers deployed by customers of a major telecom company.
Krishnendu Chakrabarty received the B. Tech. degree from the Indian Institute of Technology, Kharagpur, in 1990, and the M.S.E. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 1992 and 1995, respectively. He is now the William H. Younger Distinguished Professor and Department Chair of Electrical and Computer Engineering, and Professor of Computer Science, at Duke University. He is also a Hans Fischer Senior Fellow at the Institute for Advanced Study, Technical University of Munich, Germany.
Prof. Chakrabarty is a recipient of the National Science Foundation CAREER award, the Office of Naval Research Young Investigator award, the Humboldt Research Award from the Alexander von Humboldt Foundation, Germany, the IEEE Transactions on CAD Donald O. Pederson Best Paper Award (2015), the ACM Transactions on Design Automation of Electronic Systems Best Paper Award (2017), and over a dozen best paper awards at major conferences. He is also a recipient of the IEEE Computer Society Technical Achievement Award (2015), the IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award (2017), and then Semiconductor Research Corporation Technical Excellence Award (2018). He is a recipient of the Japan Society for the Promotion of Science (JSPS) Fellowship in the "Short Term S-Nobel Prize level" category.
Prof. Chakrabarty's current research projects include: testing and design-for-testability of integrated circuits and systems; microfluidic biochips and cyberphysical systems; data analytics for fault diagnosis, failure prediction, anomaly detection, and hardware security; neuromorphic computing systems. He is a Fellow of ACM, a Fellow of IEEE, and a Golden Core Member of the IEEE Computer Society. He is a recipient of the 2008 Duke University Graduate School Dean's Award for excellence in mentoring, and the 2010 Capers and Marion McDonald Award for Excellence in Mentoring and Advising, Pratt School of Engineering, Duke University. He has served as a Distinguished Visitor of the IEEE Computer Society (2005-2007, 2010-2012), a Distinguished Lecturer of the IEEE Circuits and Systems Society (2006-2007, 2012-2013), and an ACM Distinguished Speaker (2008-2016).
Prof. Chakrabarty served as the Editor-in-Chief of IEEE Design & Test of Computers during 2010-2012 and ACM Journal on Emerging Technologies in Computing Systems during 2010-2015. Currently he serves as the Editor-in-Chief of IEEE Transactions on VLSI Systems.
Thursday, September 6 14:20 - 14:40
Thursday, September 6 14:40 - 16:45
T2A: Special Session: ML/AI in Startups and Government Agencies
Deep Learning Architectures: An Industry and Government Agencies Perspective
The trend of migrating deep learning from the data center to the edge of the network is driving the need for specialized, low-power machine learning SoCs. These systems are being deployed in applications that require enhanced AI capabilities like ADAS systems in vehicles, home and industrial robotics and all manner of consumer electronics. Indeed, there is unlikely to be a single application or industry vertical that does not have an AI roadmap of some kind. This trend is disrupting the maturing semiconductor industry that was, until recently, suffering from a severe lack of investment for innovation. Within a few years, a multitude of new and innovative architectures for deep learning acceleration have appeared on the market. This session provides an overview of some of the work in semiconductor startup companies as well as an introduction to the MLPerf benchmarking activity that aims to assist with the AI hardware procurement process. The representatives from NSF and DARPA will go over some of the exciting new initiatives and programs coming from government in support of ML/AI activities.
During this special session, machine learning experts from Wave Computing, Syntiant, Novumind, DARPA and NSF will discuss these AI acceleration challenges, as well as their solutions and architecture implementations.
T2B: Design Track
- 14:40 DFT, PD & PO Aspects of Xavier - Flagship SOC for Autonomous Driving and Deep Learning
- 15:05 Integrated Surround & CMS Automotive SoC
- 15:30 10T Differential-Signal SRAM Design in a 14-Nm FinFET Technology for High-Speed Application
- 15:55 Smart Silicon Substrate for Quick Time to Market Chip-Stacks and Systems-in-Package
- 16:20 Low Power 20 Gbps Type-C USB3.2/DP1.4/Thunderbolt3 Combo Linear Redriver in 0.25 Μm BiCMOS Technology
Thursday, September 6 16:45 - 18:00
Panel Discussion: ML/AI in Startups and Government Agencies
Deep Learning Architectures: An Industry and Government Agencies Perspective
The trend of migrating deep learning from the data center to the edge of the network is driving the need for specialized, low-power machine learning SoCs. These systems are being deployed in applications that require enhanced AI capabilities like ADAS systems in vehicles, home and industrial robotics and all manner of consumer electronics. Indeed, there is unlikely to be a single application or industry vertical that does not have an AI roadmap of some kind. This trend is disrupting the maturing semiconductor industry that was, until recently, suffering from a severe lack of investment for innovation. Within a few years, a multitude of new and innovative architectures for deep learning acceleration have appeared on the market. This panel will answer questions about some of the work in semiconductor startup companies as well as an introduction to the MLPerf benchmarking activity that aims to assist with the AI hardware procurement process. The representatives from NSF and DARPA will go over some of the exciting new initiatives and programs coming from government in support of ML/AI activities.
During this panel, machine learning experts from Wave Computing, Syntiant, Novumind, DeePhi, DARPA and NSF will answer questions about these AI acceleration challenges, as well as their solutions and architecture implementations.
Thursday, September 6 18:30 - 21:00
Banquet with Banquet Speech
Titan IC, is a University start-up company, established with the mission to commercialise academic research in high-performance content processing. In this talk Prof Sezer will introduce Titan IC and the underpinning technologies based on academic research, targeted technology markets and business engagements with top Silicon Valley companies. The second part of the talk will address challenges faced by the founders in establishing a high-tech start-up dealing with productization of IP, raising funding, developing a successful business model, operations, marketing and customer engagements. The final part of the talk will address "lessons learned" with recommendations for academics and researchers considering a similar technology start-up.
Friday, September 7
Friday, September 7 9:00 - 10:15
F1A: MPSoC and Design Methodologies I
F1B: Low Power Circuits I
Friday, September 7 10:15 - 10:35
Friday, September 7 10:35 - 11:50
F2A: MPSoC and Design Methodologies II
F2B: Low Power Circuits II
- 10:35 A Low-Area, Low-Power, and Low-Leakage Error-Detecting Latch for Timing-Error Resilient System Designs
- 11:00 A 32kHz Crystal Oscillator Leveraging Voltage Scaling in an Ultra-Low Power 40nA Real-Time Clock
- 11:25 0.5V 10MS/s 9-Bits Asynchronous SAR ADC for BLE Receivers in 180Nm CMOS Technology