Program

FPT’22 is held as a hybrid conference. The physical venue is Room 1038, IAS building, HKUST. We use Whova as the virtual platform. You must register (there is a free option) to attend the virtual events. The program is below (subject to change). All times in Hong Kong Time (HKT), which is UTC+8.

For each paper a (pre-recorded) presentation video will be available beforehand. During the conference, each paper is presented with a live talk with Q&A session.

Overview

Time	Mon, 5 Dec Workshop/ Tutorials	Time	Tue, 6 Dec Workshop/ Tutorials	Time	Wed, 7 Dec Day 1	Time	Thu, 8 Dec Day 2	Time	Fri, 9 Dec Day 3
9:30am-1:30pm	Workshop/ Tutorials 1	9:30am-12:30pm	Workshop/ Tutorials 3	9:30am-9:50am	Opening Session	9:30am-10:30am	Keynote 2	9:30am-10:30am	Keynote 3
				9:50am-10:50am	Keynote 1		Coffee Break		Coffee Break
					Coffee Break	10:50am-12:20pm	Application 1	10:50am-11:50am	Architecture 2
				11:10am-12:20pm	AI/ML			11:50am-12:20pm	Poster Session 2
					Lunch		Lunch		Lunch
3:00pm-5:00pm	Workshop/ Tutorials 2	2:00pm-5:00pm	Workshop/ Tutorials 4	1:30pm-3:10pm	Journal Session	1:30pm-2:50pm	Tools & Design	1:30pm-3:10pm	Application 2
					Coffee Break		Coffee Break		Coffee Break
				3:30pm-4:20pm	Architecture 1	3:10pm-5:10pm	Design Competition	3:30pm-4:10pm	PhD Forum
				4:20pm-4:50pm	Poster Session 1	6:00pm-9:30pm	Banquet	4:10pm-4:30pm	Closing & Best Paper Award

Main Day 1

Time	Main Day 1: Wednesday 7^th December 2022
9:30am - 9:50am	Opening session ▸ Opening by General Chair and OC member ▸ Introduction to HKUST and ACCESS, Prof. Tim Cheng
9:50am - 10:50am	Keynote 1 (Chair: Prof. Wei Zhang) Prof. James Hoe, Carnegie Mellon University FPGA Technology at Crossroads
10:50am - 11:10am	Coffee Break
11:10am - 12:20pm	AI/ML (Chair: Prof. Oliver Diessel) ▸ Mixing Low-Precision Formats in Multiply-Accumulate Units for DNN Training, Mariko Tatsumi, Silviu-Ioan Filip, Caroline White, Olivier Sentieys and Guy Lemieux ▸ LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning, Je Yang, Jaeuk Kim and Joo-Young Kim ▸ Accelerating Transformer Neural Networks on FPGAs for High Energy Physics Experiments, Filip Wojcicki, Zhiqiang Que, Alexander D Tapper and Wayne Luk ▸ HPIPE NX: Boosting CNN Inference Acceleration Performance with AI-Optimized FPGAs, Marius Stan, Mathew Hall, Mohamed Ibrahim and Vaughn Betz ▸ Short Paper An Energy-Efficient K-means Clustering FPGA Accelerator via Most-Significant Digit First Arithmetic, Saeid Gorgin, Mohammadhosein Gholamrezaei, Danial Javaheri and Jeong-A Lee
12:20pm - 13:30pm	Lunch
1:30pm - 3:10pm	Journal Session (Chair: Dr. He Li) ▸ fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection Library, Binglei Lou, David Boland and Philip H.W. Leong ▸ Fixed-Point FPGA Implementation of the FFT Accumulation Method for Real-time Cyclostationary Analysis, Carol Jingyi Li, Xiangwei Li, Binglei Lou, Craig T.Jin, David Boland and Philip H.W.Leong ▸ High-Performance and Configurable SW/HW Co-design of Post-Quantum Signature CRYSTALS-Dilithium, Gaoyu Mao, Donglong Chen, GuangYan Li, Wangchen Dai, Abdurrashid Ibrahim Sanka, Çetin Kaya Koç and Ray C. C. Cheung ▸ Design Space Exploration of Galois and Fibonacci Configuration based on Espresso Stream Cipher, Zhengyuan Shi, Cheng Chen, Gangqiang Yang, Hailiang Xiong, Fudong Li, Honggang Hu and Zhiguo Wan ▸ FPGA Implementation of Compact Hardware Accelerators for Ring-Binary-LWE based Post-Quantum Cryptography, Pengzhou He, Tianyou Bao, Jiafeng Xie and Moeness Amin ▸ AutoScaleDSE: A Scalable Design Space Exploration Engine for High-Level Synthesis, Hyegang Jun, Hanchen Ye, Hyunmin Jeong and Deming Chen ▸ ADAS: A High Computational Utilization Dynamic Reconfigurable Hardware Accelerator for Super Resolution, Liang Chang, Xin Zhao and Jun Zhou
3:10pm - 3:30pm	Coffee Break
3:30pm - 4:20pm	Architecture 1 (Chair: Prof. Ray Cheung) ▸ A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN, Shashwat Khandelwal and Shreejith Shanker ▸ Using integer linear programming for correctly rounded multipartite architectures, Orégane Desrentes and Florent de Dinechin ▸ Energy Efficient Design of Coarse-Grained Reconfigurable Architectures: Insights, Trends and Challenges, Ensieh Aliagha and Diana Goehringer ▸ Short Paper Design Exploration of RISC-V Soft-Cores through Speculative High-Level Synthesis, Jean-Michel Gorius, Simon Rokicki and Steven Derrien
4:20pm - 4:50pm	Poster Session 1 (Chair: Dr. Wang maolin) ▸ A Highly Customizable and Efficient Hardware Implementation for Parallel Matrix Inversion, Sultan Alqahtani, Yiqun Zhu, Qizhi Shi, Xiaolin Meng and Xinhua Wang ▸ GraFF: A Multi-FPGA System with Memory Semantic Fabric for Scalable Graph Processing, Xu Zhang, Yisong Chang, Tianyue Lu, Ke Liu, Ke Zhang and Mingyu Chen ▸ Elastic Sample Filter: An FPGA-based Accelerator for Bayesian Network Structure Learning, Ryota Miyagi, Ryota Yasudo, Kentaro Sano and Hideki Takase ▸ The Impact of Hardware Folding on Dependability in Spaceborne FPGA-based Neural Networks, Ioanna Souvatzoglou, Dimitris Agiakatsikas, George Antonopoulos, Vasileios Vlagkoulis, Aitzan Sari, Athanasios Papadimitriou and Mihalis Psarakis ▸ NetPU: Prototyping a Generic Reconfigurable Neural Network Accelerator Architecture, Yuhao Liu, Shubham Rai, Salim Ullah and Akash Kumar

Main Day 2

Time	Main Day 2: Thursday 8^th December 2022
9:30am - 10:30am	Keynote 2 (Chair: Prof. Yun Eric Liang) Prof. Wang Yu, Tsinghua University Heterogeneous Acceleration for Multi-DNN workloads: Progress and Trends
10:30am - 10:50am	Coffee Break
10:50am - 12:20pm	Application 1 (Chair: Prof. Jiafeng Xie) ▸ SALIENT: Ultra-Fast FPGA-based Short Read Alignment, Behnam Khaleghi, Tianqi Zhang, Cameron Martino, George Armstrong, Ameen Akel, Ken Curewitz, Justin Eno, Sean Eilert, Rob Knight, Niema Moshiri and Tajana Rosing; Best Paper CandidateArtifact: Available ▸ Bandwidth-Efficient Homomorphic Encrypted Matrix Vector Multiplication Accelerator on FPGA, Yang Yang, Sanmukh R. Kuppannagari, Rajgopal Kannan and Viktor Prasanna ▸ Hypersort: High-performance Parallel Sorting on HBM-enabled FPGA, Soundarya Jayaraman, Bingyi Zhang and Viktor Prasanna ▸ ZHW: A Numerical CODEC for Big Data Scientific Computation, Michael Barrow, Zhuanhao Wu, Scott Lloyd, Maya Gokhale, Hiren Patel and Peter LindstromArtifact: Available, Evaluated Functional ▸ Boosting Domain-Specific Debug Through Inter-frame Compression, Zakary Nafziger, Martin Chua, Daniel Holanda Noronha and Steven J.E. WiltonArtifact: Available ▸ Short Paper Parallel CRC On An FPGA At Terabit Speeds, Qianfeng Shen, Juan Camilo Vega and Paul ChowArtifact: Available ▸ Short Paper A Cautionary Note on Building Multi-tenant Cloud-FPGA as a Secure Infrastructure, Yukui Luo, Yuheng Zhang, Shijin Duan and Xiaolin Xu
12:20pm - 13:30pm	Lunch
1:30pm - 2:50pm	Tools & Design (Chair: Prof. Kan shi) ▸ FADEC: FPGA-based Acceleration of Video Depth Estimation by HW/SW Co-design, Nobuho Hashimoto and Shinya Takamaeda-YamazakiArtifact: Available, Evaluated Functional, Evaluated Reusable, Results Replicated ▸ Automated Generation and Orchestration of Stream Processing Pipelines on FPGAs, Kaspar Matas, Kristiyan Manev, Joseph Powell and Dirk Koch ▸ P3Net: PointNet-based Path Planning on FPGA, Keisuke Sugiura and Hiroki Matsutani ▸ byteman: A Bitstream Manipulation Framework, Kristiyan Manev, Joseph Powell, Kaspar Matas and Dirk Koch; Best Paper Candidate ▸ Short Paper Area-Efficient Memory Scheduling for Dynamically Scheduled High-Level Synthesis, Xuefei He, Jianyi Cheng and George ConstantinidesArtifact: Available, Evaluated Functional, Evaluated Reusable, Results Replicated ▸ Short Paper Efficient Reinforcement Learning Framework for Automated Logic Synthesis Exploration, Yu Qian, Xuegong Zhou, Hao Zhou and Lingli Wang
2:50pm - 3:10pm	Coffee Break
3:10pm - 5:10pm	Design Competition: Autonomous Vehicle Driving using FPGAs (Chair: Prof. Minoru Watanabe) The competition will be held in a separate venue at Okayama University, Japan, and will be real-time broadcasted through the conference virtual platform. People in the physical venue in Hong Kong can also watch the live stream! Check the link for more infomation! ▸ Hardware SAT Solver-based Area-efficient Accelerator for Autonomous Driving, Yusuke Inuma and Yuko Hara-Azumi ▸ A Lane Detection Hardware Algorithm Based on Helmholtz Principle and Its Application to Unmanned Mobile Vehicles, Katsuaki Kamimae, Shintaro Matsui, Yasutoshi Araki, Takehiro Miura, Keigo Motoyoshi, Keizo Yamashita, Haruta Ikehara, Takuho Kawazu, Huang Yuwei, Masahiro Nishimura, Shuto Abe, Kenyu Okino, Yuta Hashiguchi, Koki Fukuda, Kengo Yanagihara, Taito Manabe and Yuichiro Shibata ▸ Desgin and Implementation of ROS2-based Autonomous Tiny Robot Car with Integration of Multiple ROS2 FPGA Nodes, Hayato Mori, Hayato Amano, Akinobu Mizutani, Eisuke Okazaki, Yuki Konno, Kohei Sada, Tomohiro Ono, Yuma Yoshimoto, Hakaru Tamukoh, Takeshi Ohkawa and Midori Sugaya ▸ Autonomous driving system with feature extraction using a binarized autoencoder, Kota Hisafuru, Ryotaro Negishi, Soma Kawakami, Dai Sato, Kazuki Yamashita, Keisuke Fukada and Nozomu Togawa ▸ Implementation and Improvement of Autonomous Robot Car using Soc FPGA with DPU, Akira Kojima
6:00pm - 9:30pm	Banquet

Main Day 3

Time	Main Day 3: Friday 9^th December 2022
9:30am - 10:30am	Keynote 3 (Chair: Prof. Hiroki Nakahara) Prof. Taisuke Boku How FPGA can compensate with High Performance Computing
10:30am - 10:50am	Coffee Break
10:50am - 11:50am	Architecture 2 (Chair: Dr. Zhe lin) ▸ CAPI-Precis: Towards a Compute-Centric Interface for Coherent Shared Memory Accelerators, Abdullah Mughrabi and Greg Byrd ▸ Cloning the Unclonable: Physically Cloning an FPGA RO PUF, Hayden Cook, Jonathan Thompson, Zephram Tripp, Brad Hutchings and Jeffrey Goeders; Best Paper Candidate ▸ Fast and Flexible FPGA development using Hierarchical Partial Reconfiguration, Dongjoon Park, Yuanlong Xiao and Andre DeHon, Artifact: Available, Evaluated Functional, Evaluated Reusable, Results Replicated ▸ Short Paper Leveraging FPGA Primitives to Improve Word Reconstruction during Netlist Reverse Engineering, Reilly McKendrick, Corey Simpson, Brent Nelson and Jeffrey Goeders ▸ Short Paper Exploring Inter-tile connectivity for HPC-oriented CGRA with Lower Resource Usage, Boma Adhi, Carlos Cortes, Tomohiro Ueno, Kentaro Sano, Yiyu Tan, Takuya Kojima and Artur Podobas
11:50am - 12:20pm	Poster Session 2 (Chair: Dr. Wang maolin) ▸ A Masked Pure-Hardware Implementation of Kyber Cryptographic Algorithm, Tendayi Kamucheka, Alexander Nelson, David Andrews and Miaoqing Huang ▸ FPGA implementation of HDR synthesis processing with image compression techniques, Nisimura Masahiro, Imamura Yuta, Manabe Taito and Shibata Yuichiro ▸ EXPRESS: CNN EXecution Time PREdiction for DPU DeSign Space Exploration, Shikha Goel, Rajesh Kedia, M. Balakrishnan and Rijurekha Sen ▸ LCAM: Low-Cost Approximate Multiplier Design on FPGA, Mingyu Shu and Qiang Liu ▸ ESSPER: Elastic and Scalable System for High-Performance Reconfigurable Computing with Software-bridged APIs, Kentaro Sano, Atsushi Koshiba, Takaaki Miyajima and Tomohiro Ueno
12:10pm - 13:30pm	Lunch
1:30pm - 3:10pm	Application 2 (Chair: Prof. Jieru Zhao) ▸ FSLAM: an Efficient and Accurate SLAM Accelerator on SoC FPGA, Vibhakar Vemulapati and Deming Chen ▸ Hardware-Efficient FPGA-Based Approximate Multipliers for Error-Tolerant Computing, Yao Shangshang and Zhang LiangArtifact: Available, Evaluated Functional, Results Replicated ▸ FPGA Implementation of Low-Latency Recursive Median Filter, Bo Peng, Yuzhu Zhou, Qiang Li, Maosong Lin, Jiankui Weng and Qiang Zeng ▸ Memory-efficient RMT Matching Optimization Based on MBitTree, Liu Zhongpei, Lv Gaofeng, Wang Jichang and Yang XiangruiArtifact: Available, Evaluated Functional, Results Replicated ▸ Load-Store Queue Sizing for Efficient Dataflow Circuits, Jiantao Liu, Carmine Rizzi and Lana JosipovicArtifact: Available ▸ Short Paper Dual-Triangular QR Decomposition with Global Acceleration and Partially Q-Rotation Skipping, Rui Fang, Siyang Jiang, Hsi-Wen Chen, Wei Ding and Ming-Syan Chen ▸ Short Paper Acceleration of Fast Sample Entropy Towards Biomedical Applications on FPGAs, Chao Chen, Bruno da Silva, Jianqi Li and Chengyu Liu ▸ Short Paper pLPAQ: Accelerating LPAQ Compression on FPGA, Dongdong Tang, Xuan Sun, Nan Guan, Tei Wei Kuo and Chun Jason Xue
3:10pm - 3:30pm	Coffee Break
3:30pm - 4:10pm	PhD Forum (Chair: Prof. Guojie Luo) ▸ An Agile Tile-based Platform for Adaptive Heterogeneous Many-Core Systems, Ahmed Kamaledin Atef and Diana Goehringer ▸ Application Specific Instruction-Set Processors for Machine Learning Applications, Muhammad Ali and Diana Göhringer ▸ Modeling FPGA-based Architectures for Robotics Systems, Ariel Podlubne and Diana Goehringer ▸ A Markovian Approach for Detecting Failures in the Xilinx SEM core, Trishna Rajkumar and Johnny Öberg ▸ Quality & Generality: A Flexible FPGA Re-Clustering Technique to Improve Packing and Placement, Mohamed A. Elgammal and Vaughn Betz
4:10pm - 4:30pm	Closing & Best Paper Award

Opening Session Speakers

Prof. Tim Cheng, The Hong Kong University of Science and Technology (HKUST)

Bio: Professor Tim Cheng is Vice-President for Research and Development (VPRD) and Chair Professor jointly in the Electronic & Computer Engineering (ECE) and Computer Science & Engineering (CSE) Departments at The Hong Kong University of Science and Technology (HKUST). He is also the Director of InnoHK AI Chip Center for Emerging Smart Systems which aims to advance IC design to help realize ubiquitous AI applications in society.

At HKUST, Prof. Cheng served as Dean of Engineering for 6 years prior to taking the current role of VPRD. Prior to joining HKUST in 2016, he was on the faculty of the University of California, Santa Barbara where he also served in various administrative roles including ECE Department Chair and Associate Vice-Chancellor for Research.

An internationally leading researcher with extensive experience in fostering cross-disciplinary research collaboration, Prof. Cheng is a world authority in the field of electronics design verification and testing, as well as covering a wide range of research areas including design automation of electronic and photonic systems, computer vision, and medical image analysis.

Keynote Speakers

Prof. James C. Hoe, Carnegie Mellon University

Title: FPGA Technology at Crossroads

Abstract: Field Programmable Gate Arrays (FPGAs) have been undergoing rapid and dramatic changes fueled by their expanding use in datacenter and machine learning. Rather than serving as a compromise or alternative to ASICs, modern FPGA 'programmable logic' is emerging as a third paradigm of compute that stands apart from traditional hardware vs. software archetypes. The Intel/VMware Crossroads 3D-FPGA Academic Research Center is a multi-university collaborative research to define a new role for programmable logic in future datacenter servers. Guided by both the demands of modern network-driven, data-centric computing and the new capabilities from 3D integration, this center is developing the Crossroads 3D-FPGA as a new central fixture component on future server motherboards, serving to connect all server endpoints (network, storage, memory, CPU) intelligently. As a literal crossroads of data, a Crossroads 3D-FPGA can apply application-specific functions over data-on-the-move between any pair of server endpoints, intelligently steer data to the right core or accelerator, and reduce the volume of data that needs to be moved between servers. This talk will overview the Crossroads 3-D FPGA concepts, as well as the associated set of research thrusts to pursue a full-stack solution spanning application, programming support, dynamic runtime, design automation, and architecture.

Bio: James C. Hoe is a Professor of Electrical and Computer Engineering at Carnegie Mellon University. He received his Ph.D. in EECS from Massachusetts Institute of Technology in 2000 (S.M., 1994). He received his B.S. in EECS from UC Berkeley in 1992. He is interested in many aspects of computer architecture and digital hardware design, including the specific areas of FPGA architecture for computing; digital signal processing hardware; and high-level hardware design and synthesis. He is the lead PI of the Intel/VMware Crossroads 3D-FPGA Academic Research Center. He is a Fellow of IEEE. For more information, please visit the link.

Prof. Yu Wang, Tsinghua University

Title: Heterogeneous Acceleration for Multi-DNN workloads: Progress and Trends

Abstract: We have witnessed the rapid growth of heterogeneous domain specific acceleration for deep neural networks (DNNs) in the past decade. For general artificial intelligence (AI) scenarios, especially autonomous driving and cloud computing, the computing power of AI chips is moving toward hundreds or thousands of tera operations per second (TOPS). Meanwhile, the number of DNN models is also increasing, and their types are diversifying. Currently, there is still a gap between single-model latency-optimized AI chips and multi-model versatility-oriented application requirements, leading to severe resource underutilization and sub-optimal system performance.

This talk will first examine the challenges of supporting multi-DNN workloads on traditional monolithic DNN accelerators. Second, this talk will present recent progress of enabling multi-tenancy in the architecture design of DNN accelerators. Third, this talk will envision a future where a coordinated architecture, scheduling, and mapping optimization approach would provide a great improvement on heterogeneous acceleration for multi-DNN workloads.

Bio: Yu Wang, professor, IEEE fellow, chair of the Department of Electronic Engineering of Tsinghua University, dean of Institute for Electronics and Information Technology in Tianjin, and vice dean of School of information science and technology of Tsinghua University. His research interests include the application specific heterogeneous computing, processing-in-memory, intelligent multi-agent system, and power/reliability aware system design methodology. Yu Wang has published more than 80 journals (56 IEEE/ACM journals) and 200 conference papers in the areas of EDA, FPGA, VLSI Design, and Embedded Systems, with the Google citation more than 14,000. He has received four best paper awards and 11 best paper nominations. Yu Wang has been an active volunteer in the design automation, VLSI, and FPGA conferences. He will serve as TPC chair for ASP-DAC 2025. He serves as the editor of important journals in the field such as ACM TODAES and IEEE TCAD and program committee member for leading conferences in the top EDA and FPGA conferences.

Prof. Taisuke Boku, University of Tsukuba

Title: How FPGA can compensate with High Performance Computing

Abstract: Today's HPC (High Performance Computing) is supported by accelerator technology to provide very high performance/power ratio which is not enough by general purpose CPUs. The most popular player as the accelerators is GPU which provides highly parallel computing elements under efficient manner to concentrate a large part of power consumption just for computation while many-core CPUs need a large portion for complicated instruction control.

Although the latest GPU's absolute performance is so high to overcome 50TFLOPS per device, it is mainly usable for high degree of "spatial" parallelism of the target applications to be applied SIMD (Single Instruction Multiple Data) manner control. In some applications which are constructed with multiple phenomena with variation of partial algorithm and degree of parallelism, GPU computation is not perfect, and these even small fraction of computation bottlenecks the total performance. We have been researching how FPGA can compensate with GPU to attack such a problem. It is well known that the absolute performance (FLOPS) of FPGA is far from the advanced GPUs, however the computation model of FPGA is based on the combination of pipeline parallelism and spatial one, and we can expect very high efficiency when combining these devices for a single application.

In this talk, I will introduce our concept of such a multi-hybrid acceleration named CHARM (Cooperative Hybrid Acceleration with Reconfigurable Multi-devices), and the hardware and software systems we have been developing as well as a typical application which is accelerated one order of magnitude from GPU-only solution. We are running a supercomputer named Cygnus which is the world first multi-hybrid accelerated system with GPU and FPGA coupling, and I also introduce how the system is used now.

Bio: Taisuke Boku has been researching HPC system architecture, system software, and performance evaluation on various scientific applications after he received PhD degree of Electrical Engineering from Keio University, Japan. He is currently the director of Center for Computational Sciences, University of Tsukuba, a co-designing center with both application researchers and HPC system researchers. He has been playing a central roles for development of original supercomputers in the center including CP-PACS (ranked as number one in TOP500 in 1996), FIRST, PACS-CS, HA-PACS and Cygnus systems, the representative supercomputers in Japan. The recent system Cygnus is the world first multi-hybrid accelerated system with GPU and FPGA together. He has been the President of HPCI (High Performance Computing Infrastructure) Consortium in Japan in 2020-2022. He was a member of system architecture working group of Fugaku supercomputer development. He received ACM Gordon Bell Prize in 2011.

Workshop/Tutorials

Workshop/Turtorial Chairs: Sharad Sinha (IIT Goa) and Peter Chun (Huawei)

Title: OpenFPGA

Organizers: Pierre-Emmanuel Gaillardon (University of Utah, OSFPGA Foundation), Nanditha Rao (IIIT Bangalore), Tony McDowell (OSFPGA Foundation) and Aman Arora (University of Texas, Austin)

Description: Create your own FPGA! Check more details at the link. Note that there is a separate registration entry for this workshop, please kindly see this form.

Date and Time: 5th December 2022, 9:30am - 1:30pm

Mode: Online (Live link is available in the virtual platform)

Title: Using Intel FPGA High-Level Synthesis Interfaces, a Hands-On Tutorial

Organizers: Intel Labs

Description: The Intel® HLS Compiler is a high-level synthesis (HLS) tool that takes in untimed C++ as input and generates production-quality Verilog code that is optimized for Intel® FPGAs. In this tutorial, you will learn about the various component interfaces that can be generated by the Intel® HLS Compiler and how to infer them in your code. The interfaces covered include conduit interfaces, and Avalon memory-mapped host and agent interfaces, and Avalon streaming interfaces. The attendees will perform a hands-on lab that will give them practice generating components with different interfaces.

Date and Time: 5th December 2022, 3:00pm - 5:00pm

Mode: Online (All registered attendees can join the workshop and a Teams meeting link is available in the virtual platform)

Title: Enabled Car Plate Recognition using AI – Explore the Power of Xilinx SoC

Organizers: AVNET and HKSTP

Description: This 3-hours course will help engineers jump start the development of an AI design on Xilinx SoC device using Avnet Ultra96 development board, a cost-optimized development platform for embedded vision and industrial IoT systems. The workshop will introduce Xilinx MPSOC device, Vitis AI tools, AI ModelZoo and deploy Automatic Number Plate Recognition (ANPR) on Avnet Ultra96 board. Through simple step-by-step procedure, participants will complete an AI design on an embedded processor in just few hours! Go register here!

Date and Time: 6th December 2022, 9:30am - 12:30pm

Mode: Online training, and live link is available in the virtual platform

Title: Enable Research with Heterogeneous Accelerated Compute Clusters

Organizers: AMD Xilinx, NUS

Description:

Heterogeneous Accelerated Compute Clusters (HACC) Introduction
Parallel Graph Processing Accelerators on FPGAs, Dr. Yao Chen, Assistant Professor, NUS
Improving Energy Efficiency of Permissioned Blockchains Using FPGAs, Dr. Haris Javaid, SMTS, AMD Xilinx
HACC Tutorial, Dr. Hongshi Tan, NUS

Date and Time: 6th December 2022, 2pm - 5pm

Mode: Online (All registered attendees can join the workshop and a Zoom meeting link is available in the virtual platform)