Zhiru Zhang

2024

• [C96] H. Chen, C. H. Yu, S. Zheng, Z. Zhang, Z. Zhang, and Y. Wang, Slapo: A Schedule Language for Efficient Progressive Optimization of Large Deep Learning Model Training, International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Apr./May 2024.
• [C95] Y. Li, N. Lazarev, D. Koufaty, Y. Yin, A. Anderson, Z. Zhang, G. E. Suh, K. Kaffes, and C. Delimitrou, LibPreemptible: Enabling Fast, Adaptive, and Hardware-Assisted User-Space Scheduling, International Symposium on High-Performance Computer Architecture (HPCA), Mar. 2024.

2023

• [C94] Y. Zhang, A. Garg, Y. Cao, L. Lew, B. Ghorbani, Z. Zhang, and O. Firat, Binarized Neural Machine Translation, Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), Dec. 2023.
• [J19] L. Guo, Y. Chi, J. Lau, L. Song, X. Tian, M. Khatti, W. Qiao, J. Wang, E. Ustun, Z. Fang, Z. Zhang, J. Cong, TAPA: A Scalable Task-Parallel Dataflow Programming Framework for Modern FPGAs with Co-Optimization of HLS and Physical Design, ACM Transactions on Reconfigurable Technology and Systems (TRETS), 2023.
• [C93] E. S. Alcorta, A. Gerstlauer, C. Deng, Q. Sun, Z. Zhang, C. Xu, L. W. Wills, D. S. Lopera, W. Ecker, S. Garg, and J. Hu, Machine Learning for Embedded System Design, International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), Sep. 2023. (Invited Paper)
• [C92] N. Lazarev, T. Ji, A. Kalia, D. Kim, I. Marinos, F. Y. Yan, C. Delimitrou, Z. Zhang, and A. Akella, Resilient Baseband Processing in Virtualized RANs with Slingshot, Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM), Sep. 2023.
• [J18] L. Guo, P. Maidee, Y. Zhou, C. Lavin, E. Hung, W. Li, J. Lau, W. Qiao, Y. Chi, L. Song, Y. Xiao, A. Kaviani, Z. Zhang, and J. Cong, RapidStream 2.0: Automated Parallel Implementation of Latency Insensitive FPGA Designs Through Partial Reconfiguration, ACM Transactions on Reconfigurable Technology and Systems (TRETS), Sep. 2023.
• [J17] S. K. Venkataramanaiah, J. Meng, H. Suh, I. Yeo, J. Saikia, S. K. Cherupally, Y. Zhang , Z. Zhang, and J. Seo, A 28nm 8-bit Floating-Point Tensor Core based CNN Training Processor with Dynamic Activation/Weight Sparsification, IEEE Journal of Solid-State Circuits (JSSC), Jul. 2023.
• [C91] E. Ustun, C. Yu, and Z. Zhang, Equality Saturation for Datapath Synthesis: A Pathway to Pareto Optimality, Design Automation Conference (DAC), Jul. 2023. (Invited Perspective Paper)
• [J16] J. Liu, Z. Zhao, Z. Ding, B. Brock, H. Rong, and Z. Zhang, An Intermediate Language for General Sparse Format Customization, IEEE Computer Architecture Letters (CAL), Mar. 2023.
• [C90] Z. Zhang, M. Hofmann, and A. Butt, A Case for Open EDA Verticals, International Symposium on Physical Design (ISPD), Mar. 2023. (Invited Perspective Paper)

2022

• [C89] C. Deng, X. Li, Z. Feng, and Z. Zhang, GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks, Learning on Graphs Conference (LoG), Dec. 2022. (Spotlight)
• [C88] T. Yu, Y. Zhang, Z. Zhang, and C. De Sa, Understanding Hyperdimensional Computing for Parallel Single-Pass Learning, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), Nov./Dec. 2022.
• [J15] J. Cong, J. Lau, G. Liu, S. Neuendorffer, P. Pan, K. Vissers, and Z. Zhang, FPGA HLS Today: Successes, Challenges, and Opportunities, ACM Transactions on Reconfigurable Technology and Systems (TRETS), Dec. 2022. (Keynote Paper)
• [B3] D. Pal, C. Deng, E. Ustun, C. Yu, and Z. Zhang, Machine Learning for Agile FPGA Design, Machine Learning Applications in Electronic Design Automation, ed. H. Ren and J. Hu, Springer, Aug. 2022.
• [J14] W. Hua, Z. Zhang, and G. E. Suh, Reverse Engineering CNN Models using Side-Channel Attacks, IEEE Design & Test, Aug. 2022. (IEEE HSTTC Top Picks in Hardware and Embedded Security)
• [C87] W. Hua, M. Umar, Z. Zhang, and G. E. Suh, GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning, Design Automation Conference (DAC), Jul. 2022.
• [C86] D. Pal, Y.-H. Lai, S. Xiang, N. Zhang, H. Chen, J. Casas, P. Cocchini, Z. Yang, Jin Yang, L.-N. Pouchet, and Z. Zhang, Accelerator Design with Decoupled Hardware Customizations: Benefits and Challenges, Design Automation Conference (DAC), Jul. 2022. (Invited Paper)
• [C85] Y. Zhang, Z. Zhang, and L. Lew, PokeBNN: A Binary Pursuit of Lightweight Accuracy, The Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2022.
• [C84] M. Umar, W. Hua, Z. Zhang, and G. E. Suh, SoftVN: Efficient Memory Protection via Software-Provided Version Numbers, International Symposium on Computer Architecture (ISCA), Jun. 2022.
• [C83] W. Hua, M. Umar, Z. Zhang, and G. E. Suh, MGX: Near-Zero Overhead Memory Protection for Data-Intensive Accelerators, International Symposium on Computer Architecture (ISCA), Jun. 2022.
• [J13] L. Cheng, P. Pan, Z. Zhao, K. Ranjan, J. Weber, B. Veluri, S. Ehsani, M. Ruttenberg, D. Jung, P. Ivanov, D. Richmond, M. Taylor, Z. Zhang, and C. Batten, A Tensor Processing Framework for CPU-Manycore Heterogeneous Systems, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Jun. 2022.
• [C82] E. Ustun, I. San, J. Yin, C. Yu, and Z. Zhang, IMpress: Large Integer Multiplication Expression Rewriting for FPGA HLS, International Symposium on Field-Programmable Custom Computing Machines (FCCM), May 2022.
• [C81] S. Xiang, Y.-H. Lai, Y. Zhou, H. Chen, N. Zhang, D. Pal, and Z. Zhang, HeteroFlow: An Accelerator Programming Model with Decoupled Data Placement for Software-Defined FPGAs, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb./Mar. 2022.
• [C80] Y. Du, Y. Hu, and Z. Zhang, High-Performance Sparse Linear Algebra on HBM-Equipped FPGAs Using HLS: A Case Study on SpMV, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb./Mar. 2022.
• [C79] L. Guo, P. Maidee, Y. Zhou, C. Lavin, J. Wang, Y. Chi, W. Qiao, A. Kaviani, Z. Zhang, and J. Cong, RapidStream: Parallel Physical Implementation of FPGA HLS Designs, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb./Mar. 2022. (Best Paper Award)
• [B2] N. Srivastava, G. Liu, Y.-H. Lai, and Z. Zhang, FPGA-Specific Compilers, Handbook of Computer Architecture, ed. A. Chattopadhyay, Springer, Jan. 2022.

2021

• [C78] W. Hua, Y. Zhang, C. Guo, Z. Zhang, and G. E. Suh, BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining, Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), Dec. 2021.
• [J14] Y.-H. Lai, E. Ustun, S. Xiang, Z. Fang, H. Rong, and Z. Zhang, Programming and Synthesis for Software-Defined FPGA Acceleration: Status and Future Prospects, ACM Transactions on Reconfigurable Technology and Systems (TRETS), Dec. 2021. (Invited Paper)
• [C77] Y. Zhou, H. Wang, J. Yin, and Z. Zhang, Distilling Arbitration Logic from Traces using Machine Learning: A Case Study on NoC, Design Automation Conference (DAC), Dec. 2021. (Best Paper Nominee)
• [C76] Y. Hu, Y. Du, E. Ustun, and Z. Zhang, GraphLily: Accelerating Graph Linear Algebra on HBM-Equipped FPGAs, International Conference On Computer Aided Design (ICCAD), Nov. 2021.
• [C75] S. Chattopadhyay, F. Lonsing, L. Piccolboni, D. Soni, P. Wei, X. Zhang, Y. Zhou, L. Carloni, D. Chen, J. Cong, R. Karri, Z. Zhang, C. Trippel, C. Barrett, and S. Mitra, Scaling Up Hardware Accelerator Verification using A-QED with Functional Decomposition, Twenty-first Conference on Formal Methods in Computer-Aided Design (FMCAD), Oct. 2021.
• [J12] C. Hao, J. Dotzel, J. Xiong, L. Benini, Z. Zhang, and D. Chen, Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design, IEEE Design & Test, Aug. 2021.
• [C74] W. Cheng, C. Deng, Z. Zhao, Y. Cai, Z. Zhang, and Z. Feng, SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation, International Conference on Machine Learning (ICML), Jul. 2021.
• [C73] N. Lazarev, S. Xiang, N. Adit, Z. Zhang, and C. Delimitrou, Dagger: Efficient and Fast RPCs in Cloud Microservices with Near-Memory Reconfigurable NICs, International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Apr. 2021. (IEEE Micro Top Picks Honorable Mention)
• [C72] Y. Zhang, J. Pan, X. Liu, H. Chen, D. Chen, and Z. Zhang, FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb./Mar. 2021. (Best Paper Nominee)
• [C71] L. Guo, Y. Chi, J. Wang, J. Lau, W. Qiao, E. Ustun, Z. Zhang, and J. Cong, AutoBridge: Coupling Coarse-Grained Floorplanning and Pipelining for High-Frequency HLS Design on Multi-Die FPGAs, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb./Mar. 2021. (Best Paper Award)
• [C70] J. Jiao, D. Pal, C. Deng, and Z. Zhang, GLAIVE: Graph Learning Assisted Instruction Vulnerability Estimation, Design, Automation and Test in Europe Conference (DATE), Feb. 2021.

• [C69] Y. Hu, Z. Ye, M. Wang, J. Yu, D. Zheng, M. Li, Z. Zhang, Z. Zhang, and Y. Wang, FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems, International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Nov. 2020.
• [C68] Y.-H. Lai, H. Rong, S. Zheng, W. Zhang, X. Cui, Y. Jia, J. Wang, B. Sullivan, Z. Zhang, Y. Liang, Y. Zhang, J. Cong, N. George, J. Alvarez, C. Hughes and P. Dubey, SuSy: A Programming Model for Productive Construction of High-Performance Systolic Arrays on FPGAs, International Conference On Computer Aided Design (ICCAD), Nov. 2020.
• [C67] E. Ustun, C. Deng, D. Pal, and Z. Zhang, Accurate Operation Delay Prediction for FPGA HLS using Graph Neural Networks, International Conference On Computer Aided Design (ICCAD), Nov. 2020.
• [C66] N. Srivastava, H. Jin, J. Liu, D. Albonesi, and Z. Zhang, MatRaptor: A Sparse-Sparse Matrix Multiplication Accelerator Based on Row-Wise Product, International Symposium on Microarchitecture (MICRO), Oct. 2020.
• [J11] N. Lazarev, N. Adit, S. Xiang, Z. Zhang, and C. Delimitrou, Dagger: Towards Efficient RPCs in Cloud Microservices with Near-Memory Reconfigurable NICs, IEEE Computer Architecture Letters (CAL), Aug. 2020.
• [C65] L. Guo, J. Lau, Y. Chi, J. Wang, C. Yu, Z. Chen, Z. Zhang, and J. Cong, Analysis and Optimization of the Implicit Broadcasts in FPGA HLS to Improve Maximum Frequency, Design Automation Conference (DAC), Jul. 2020.
• [C64] E. Singh, F. Lonsing, S. Chattopadhyay, M. Strange, P. Wei, X. Zhang, Y. Zhou, J. Cong, D. Chen, Z. Zhang, P. Raina, C. Barrett, and S. Mitra, A-QED Verification of Hardware Accelerators, Design Automation Conference (DAC), Jul. 2020.
• [C63] R. Nigam, S. Atapattu, S. Thomas, Z. Li, T. Bauer, Y. Ye, A. Koti, A. Sampson, and Z. Zhang, Predictable Design of FPGA Accelerators using Time-Sensitive Affine Types, ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), Jun. 2020. (Selected into MIT PL Review 2023)
• [C62] C. Deng, Z. Zhao, Y. Wang, Z. Zhang, and Z. Feng, GraphZoom: A Multi-Level Spectral Approach for Accurate and Scalable Graph Embedding, International Conference on Learning Representations (ICLR), Apr. 2020. (Oral Category)
• [C61] Y. Zhang, R. Zhao, W. Hua, N. Xu, G. E. Suh, and Z. Zhang, Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations, International Conference on Learning Representations (ICLR), Apr. 2020.
• [C60] N. Srivastava, H. Jin, S. Smith, H. Rong, D. Albonesi, and Z. Zhang, Tensaurus: A Versatile Accelerator for Mixed Sparse-Dense Tensor Computations, International Symposium on High-Performance Computer Architecture (HPCA), Feb. 2020.

2019

• [J10] A. Rovinski, C. Zhao, K. Al-Hawaj, P. Gao, S. Xie, C. Torng, S. Davidson, A. Amarnath, L. Vega, B. Veluri, A. Rao, T. Ajayi, J. Puscar, S. Dai, R. Zhao, D. Richmond, Z. Zhang, I. Galton, C. Batten, M. Taylor, and R. Dreslinski, Evaluating Celerity: A 16nm 695 Giga-RISC-V Instructions/s Manycore Processor with Synthesizable PLL, IEEE Solid-State Circuits Letters (SSC-L), Dec. 2019.
• [C59] W. Hua, Y. Zhou, C. De Sa, Z. Zhang, and G. E. Suh, Channel Gating Neural Networks, Thirty-third Conference on Neural Information Processing Systems (NeurIPS), Dec. 2019.
• [C58] W. Hua, Y. Zhou, C. De Sa, Z. Zhang, and G. E. Suh, Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating, International Symposium on Microarchitecture (MICRO), Oct. 2019.
• [C57] R. Zhao, Y. Hu, J. Dotzel, C. De Sa, and Z. Zhang, Building Efficient Deep Neural Networks with Unitary Group Convolutions, The Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2019.
• [C56] R. Zhao, Y. Hu, J. Dotzel, C. De Sa, and Z. Zhang, Improving Neural Network Quantization without Retraining using Outlier Channel Splitting, International Conference on Machine Learning (ICML), Jun. 2019.
• [C55] A. Rovinski, C. Zhao, K. Al-Hawaj, P. Gao, S. Xie, C. Torng, S. Davidson, A. Amarnath, L. Vega, B. Veluri, A. Rao, T. Ajayi, J. Puscar, S. Dai, R. Zhao, D. Richmond, Z. Zhang, I. Galton, C. Batten, M. Taylor, and R. Dreslinski, A 1.4 GHz 695 Giga RISC-V Inst/s 496-core Manycore Processor with Mesh On-Chip Network and an All-Digital Synthesized PLL in 16nm CMOS, Symposium on VLSI Circuits (VLSI), Jun. 2019.
• [C54] Y. Zhou, H. Ren, Y. Zhang, B. Keller, B. Khailany, and Z. Zhang, PRIMAL: Power Inference using Machine Learning, Design Automation Conference (DAC), Jun. 2019.
• [C53] C. Yu and Z. Zhang, Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets, Design Automation Conference (DAC), Jun. 2019.
• [C52] Z. Jiang, H. Jin, G. E. Suh, and Z. Zhang, Designing Secure Cryptographic Accelerators with Information Flow Enforcement: A Case Study on AES, Design Automation Conference (DAC), Jun. 2019.
• [C51] S. Dai and Z. Zhang, Improving Scalability of Exact Modulo Scheduling with Specialized Conflict-Driven Learning, Design Automation Conference (DAC), Jun. 2019.
• [C50] G. Liu, J. Primmer, and Z. Zhang, Rapid Generation of High-Quality RISC-V Processors from Functional Instruction Set Specifications, Design Automation Conference (DAC), Jun. 2019.
• [C49] N. Srivastava, H. Rong, P. Barua, G. Feng, H. Cao, Z. Zhang, D. Albonesi, V. Sarkar, W. Chen, P. Petersen, G. Lowney, A. Herr, C. Hughes, T. Mattson, and P. Dubey, T2S-Tensor: Productively Generating High-Performance Spatial Hardware for Dense Tensor Computations, International Symposium on Field-Programmable Custom Computing Machines (FCCM), Apr./May 2019.
• [C48] E. Ustun, S. Xiang, J. Gui, C. Yu, and Z. Zhang, LAMDA: Learning-Assisted Multi-Stage Autotuning for FPGA Design Closure, International Symposium on Field-Programmable Custom Computing Machines (FCCM), Apr./May 2019.
• [C47] Y.-H. Lai, Y. Chi, Y. Hu, J. Wang, C. H. Yu, Y. Zhou, J. Cong, and Z. Zhang, HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2019. (Best Paper Award)
• [J9] G. Liu and Z. Zhang, PIMap: A Flexible Framework for Improving LUT-Based Technology Mapping via Parallelized Iterative Optimization, ACM Transactions on Reconfigurable Technology and Systems (TRETS), Jan. 2019.

2018

• [C46] Z. Jiang, S. Dai, G. E. Suh, and Z. Zhang, High-Level Synthesis with Timing-Sensitive Information Flow Enforcement, International Conference On Computer Aided Design (ICCAD), Nov. 2018.
• [C45] W. Hua, Z. Zhang, and G. E. Suh, Reverse Engineering Convolutional Neural Networks Through Side-channel Information Leaks, Design Automation Conference (DAC), Jun. 2018.
• [C44] S. Dai, Y. Zhou, H. Zhang, E. Ustun, E. F.Y. Young, and Z. Zhang, Fast and Accurate Estimation of Quality of Results in High-Level Synthesis with Machine Learning, International Symposium on Field-Programmable Custom Computing Machines (FCCM), Apr./May 2018. (Best Paper Award — Short Paper Category)
• [J8] S. Davidson, S. Xie, C. Torng, K. Al-Hawai, A. Rovinski, T. Ajayi, L. Vega, C. Zhao, R. Zhao, S. Dai, A. Amarnath, B. Veluri, P. Gao, A. Rao, G. Liu, R. Gupta, Z. Zhang, R. Dreslinski, C. Batten, and M. Taylor, The Celerity Open-Source 511-Core RISC-V Tiered Accelerator Fabric: Fast Architectures and Design Methodologies for Fast Chips, IEEE Micro, Mar/Apr. 2018.
• [C43] S. Dai, G. Liu, and Z. Zhang, A Scalable Approach to Exact Resource-Constrained Scheduling Based on a Joint SDC and SAT Formulation, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2018. (Best Paper Nominee)
• [C42] Y. Zhou, U. Gupta, S. Dai, R. Zhao, N. Srivastava, H. Jin, J. Featherston, Y.-H. Lai, G. Liu, G. Velasquez, W. Wang, and Z. Zhang, Rosetta: A Realistic High-Level Synthesis Benchmark Suite for Software Programmable FPGAs, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2018.

2017

• [C41] G. Liu and Z. Zhang, Statistically Certified Approximate Logic Synthesis, International Conference on Computer-Aided Design (ICCAD), Nov. 2017.
• [C40] S. Dai, G. Liu, R. Zhao, and Z. Zhang, Enabling Adaptive Loop Pipelining in High-Level Synthesis, 52nd Annual Asilomar Conference on Signals, Systems, and Computers, Oct. 2017. (Invited Paper)
• [W4] T. Ajayi, K. Al-Hawaj, A. Amarnath, S. Dai, S. Davidson, P. Gao, G. Liu, A. Rao, A. Rovinski, N. Sun, C. Torng, L. Vega, B. Veluri, S. Xie, C. Zhao, R. Zhao, C. Batten, R. Dreslinski, R. Gupta, M. Taylor, and Z. Zhang, Experiences Using the RISC-V Ecosystem to Design an Accelerator-Centric SoC in TSMC 16nm, First Workshop on Computer Architecture Research with RISC-V (CARRV), Oct. 2017.
• [C39] T. Ajayi, K. Al-Hawaj, A. Amarnath, S. Dai, S. Davidson, P. Gao, G. Liu, A. Lotfi, J. Puscar, A. Rao, A. Rovinski, L. Salem, N. Sun, C. Torng, L. Vega, B. Veluri, X. Wang, S. Xie, C. Zhao, R. Zhao, C. Batten, R. Dreslinski, I. Galton, R. Gupta, P. Mercier, M. Srivastava, M. Taylor, and Z. Zhang, Celerity: An Open-Source RISC-V Tiered Accelerator Fabric, ACM/IEEE Symposium on High-Performance Chips (HOTCHIPS), Aug. 2017.
• [W3] J.H. Lin, T. Xing, R. Zhao, Z. Zhang, M. Srivastava, Z. Tu, and R. Gupta, Binarized Neural Networks with Separable Filters for Efficient Hardware Acceleration, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jul. 2017.
• [C38] E. Bartz, J. Chaves, Y. Gershtein, E. Halkiadakis, M. Hildreth, S. Kyriacou, K. Lannon, A. Lefeld, A. Ryd, L. Skinnari, R. Stone, C. Strohman, Z. Tao, B. Winer, P. Wittich, Z. Zhang, and M. Zientek, FPGA-based Real-time Charged Particle Trajectory Reconstruction at the Large Hadron Collider, International Symposium on Field-Programmable Custom Computing Machines (FCCM), May 2017.
• [C37] G. Liu and Z. Zhang, A Parallelized Iterative Improvement Approach to Area Optimization for LUT-Based Technology Mapping, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2017. (Best Paper Nominee)
• [C36] Y. Zhou, K. Al-Hawaj, and Z. Zhang, A New Approach to Automatic Memory Banking using Trace-Based Address Mining, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2017.
• [C35] R. Zhao, W. Song, W. Zhang, T. Xing, J.-H. Lin, M. Srivastava, R. Gupta, and Z. Zhang, Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2017.
• [C34] C. Xu, G. Liu, R. Zhao, S. Yang, G. Luo, and Z. Zhang, A Parallel Bandit-Based Approach for Autotuning FPGA Compilation, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2017.
• [C33] S. Dai, R. Zhao, G. Liu, S. Srinath, U. Gupta, C. Batten, and Z. Zhang, Dynamic Hazard Resolution for Pipelining Irregular Loops in High-Level Synthesis, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2017.
• [C32] N. Srivastava, S. Dai, R. Manohar, and Z. Zhang, Accelerating Face Detection on Programmable SoC Using C-Based Synthesis, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2017.
• [J7] G. Liu, M. Tan, S. Dai, R. Zhao, and Z. Zhang, Architecture and Synthesis for Area-Efficient Pipelining of Irregular Loop Nests, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Feb. 2017.

2016

• [J6] D. Chen, J. Cong, S. Gurumani, W-M. Hwu, K. Rupnow, and Z. Zhang, Platform Choices and Design Demands for IoT Platforms: Cost, Power and Performance Tradeoffs, IET Cyber-Physical Systems: Theory & Applications (IET-CPS), Nov. 2016.
• [C31] R. Zhao, G. Liu, S. Srinath, C. Batten, and Z. Zhang, Improving High-Level Synthesis with Decoupled Data Structure Optimization, Design Automation Conference (DAC), Jun. 2016.
• [C30] A. Majumdar, Z. Zhang, and D. Albonesi, Characterizing the Benefits and Limitations of Smart Building Meeting Room Scheduling, International Conference on Cyber-Physical Systems (ICCPS), Apr. 2016.
• [W2] D. Chen, J. Cong, S. Gurumani, W-M. Hwu, K. Rupnow, and Z. Zhang, System Synthesis and Automated Verification: Design Demands for IoT Devices, Sensors to Cloud Architectures Workshop (SCAW), Mar. 2016.

2015

• [C29] F. Koushanfar, A. Mirhoseini, G. Qu, and Z. Zhang, DA Systemization of Knowledge: A Catalog of Prior Forward-Looking Initiatives, International Conference on Computer-Aided Design (ICCAD), Nov. 2015. (Invited Paper)
• [C28] M. Tan, G. Liu, R. Zhao, S. Dai, and Z. Zhang, ElasticFlow: A Complexity-Effective Approach for Pipelining Irregular Loop Nests, International Conference on Computer-Aided Design (ICCAD), Nov. 2015.
• [C27] G. Liu and Z. Zhang, A Reconfigurable Analog Substrate for Highly Efficient Maximum Flow Computation, Design Automation Conference (DAC), Jun. 2015.
• [C26] R. Zhao, M. Tan, S. Dai, and Z. Zhang, Area-Efficient Pipelining for FPGA-Targeted High-Level Synthesis, Design Automation Conference (DAC), Jun. 2015.
• [C25] M. Tan, S. Dai, U. Gupta, and Z. Zhang, Mapping-Aware Constrained Scheduling for LUT-Based FPGAs, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2015.
• [J5] Z. Zhang, D. Chen, S. Dai, and K. Campbell, High-Level Synthesis for Low-Power Design, IPSJ Transactions on System LSI Design Methodology (T-SLDM), Feb. 2015. (Invited Paper)

2014

• [C24] S. Srinath, B. Ilbeyi, M. Tan, G. Liu, Z. Zhang, and C. Batten, Architectural Specialization for Inter-Iteration Loop Dependence Patterns, International Symposium on Microarchitecture (MICRO), Dec. 2014.
• [C23] M. Tan, B. Liu, S. Dai, and Z. Zhang, Multithreaded Pipeline Synthesis for Data-Parallel Kernels, International Conference on Computer-Aided Design (ICCAD), Nov. 2014.
• [C22] G. Liu, Y. Tao, M. Tan, and Z. Zhang, CASA: Correlation-Aware Speculative Adders, International Symposium on Low Power Electronics and Design (ISLPED), Aug. 2014.
• [C21] S. Dai, M. Tan, K. Hao, and Z. Zhang, Flushing-Enabled Loop Pipelining for High-Level Synthesis, Design Automation Conference (DAC), Jun. 2014.

2013

• [C20] Z. Zhang and B. Liu, SDC-Based Modulo Scheduling for Pipeline Synthesis, International Conference on Computer-Aided Design (ICCAD), Nov. 2013.

2012

• [C19] Z. Zhang and D. Chen, Challenges and Opportunities of ESL Design Automation, International Conference on Solid-State and Integrated Circuit Technology (ICSICT), Oct. 2012. (Invited Paper)

2011

• [J4] J. Cong, B. Liu, S. Neuendorffer, J. Noguera, K. Vissers, and Z. Zhang, High-Level Synthesis for FPGAs: From Prototyping to Deployment, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 30(4):473–491, Apr. 2011. (Keynote Paper)

2010

• [J3] J. Cong, B. Liu, R. Majumdar, and Z. Zhang, Behavior-Level Observability Analysis and Operation Gating in Low-Power Behavioral Synthesis, ACM Transactions on Design Automation of Electronic Systems (TODAES), 16(1):1–29, Nov. 2010. (Best Paper Award)
• [C18] J. Zhang, Z. Zhang, S. Zhou, M. Tan, X. Liu, X. Cheng, and J. Cong, Bit-Level Optimization for High-Level Synthesis and FPGA-Based Acceleration, International Symposium on FPGAs (FPGA), Feb. 2010.

2009

• [C17] J. Cong, B. Liu, and Z. Zhang, Scheduling with Soft Constraints, International Conference on Computer-Aided Design (ICCAD), Nov 2009. (Best Paper Nominee)
• [C16] J. Cong, B. Liu, and Z. Zhang, Behavior-Level Observability Don't-Cares and Application to Low-Power Behavioral Synthesis, International Symposium on Low Power Electronics and Design (ISLPED), Aug. 2009.
• [W1] J. Zhang, Z. Zhang, S. Zhou, M. Tan, X. Liu, X. Cheng, and J. Cong, Bit-Level Transformation and Optimization for Hardware Synthesis of Algorithmic Descriptions, International Workshop on Logic & Synthesis (IWLS), Aug. 2009.
• [C15] J. Cong, K. Gururaj, B. Liu, C. Liu, Z. Zhang, S. Zhou, and Y. Zou, Evaluation of Static Analysis Techniques for Fixed-Point Precision Optimization, IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM), Apr. 2009.

2008

• [B1] Z. Zhang, Y. Fan, W. Jiang, G. Han, C. Yang, and J. Cong, AutoPilot: A Platform-Based ESL Synthesis System, High-Level Synthesis: From Algorithm to Digital Circuit, ed. P. Coussy and A. Morawiec, Springer, 2008.
• [C14] W. Jiang, Z. Zhang, M. Potkonjnak, and J. Cong, Scheduling with Integer Time Budgeting for Low-Power Optimization, Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 2008.
• [C13] C.T. Hsieh, J. Cong, S.C. Chang, and Z. Zhang, Behavioral Synthesis with Activating Unused Flip-Flops for Reducing Glitch Power in FPGA, Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 2008.

2007

• [C12] D. Chen, J. Cong, Y. Fan, and Z. Zhang, High-Level Power Estimation and Low-Power Design Space Exploration for FPGAs, Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 2007.

2006

• [J2] J. Cong, G. Han, and Z. Zhang, Architecture and Compiler Optimization for Data Bandwidth Improvement in Configurable Processors, IEEE Transaction on Very Large Scale Integration Systems (TVLSI), 14(9):986–997, Sep. 2006.
• [C11] J. Cong, Y. Fan, G. Han, W. Jiang, and Z. Zhang, Platform-Based Behavior-Level and System-Level Synthesis, IEEE International SOC Conference (SOCC), Sep. 2006. (Invited Paper)
• [C10] J. Cong and Z. Zhang, An Efficient and Versatile Scheduling Algorithm Based on SDC Formulation, Design Automation Conference (DAC), Jul. 2006. (TCFPGA Hall of Fame — Class of 2022)
• [C9] J. Cong, Y. Fan, G. Han, W. Jiang, and Z. Zhang, Behavior and Communication Co-Optimization for Systems with Sequential Communication Media, Design Automation Conference (DAC), Jul. 2006.

2005

• [C8] J. Cong, G. Han, and Z. Zhang, Architecture and Compilation for Data Bandwidth Improvement in Configurable Embedded Processors, International Conference on Computer-Aided Design (ICCAD), Nov. 2005.
• [C7] J. Cong, Y. Fan, G. Han, A. Jagannathan, G. Reinman, and Z. Zhang, Instruction Set Extension with Shadow Registers for Configurable Processors, International Symposium on FPGAs (FPGA), Feb. 2005.
• [C6] J. Cong, Y. Fan, G. Han, Y. Lin, J. Xu, Z. Zhang, and X. Cheng, Bitwidth-Aware Scheduling and Binding in High-Level Synthesis, Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 2005.

2004

• [C5] J. Cong, Y. Fan, and Z. Zhang, Architecture-Level Synthesis for Automatic Interconnect Pipelining, Design Automation Conference (DAC), Jun. 2004.
• [J1] J. Cong, Y. Fan, G. Han, X. Yang, and Z. Zhang, Architecture and Synthesis for On-Chip Multicycle Communication, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 23(4):550–564, Apr. 2004.
• [C4] J. Cong, Y. Fan, G. Han, and Z. Zhang, Application-Specific Instruction Generation for Configurable Processor Architectures, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2004. (TCFPGA Hall of Fame — Class of 2023)

2003

• [C3] J. Cong, Y. Fan, G. Han, X. Yang, and Z. Zhang, Architectural Synthesis Integrated with Global Placement for Multi-Cycle Communication, International Conference on Computer-Aided Design (ICCAD), Nov. 2003.
• [C2] Z. Zhang, Y. Fan, M. Potkonjak, and J. Cong, Gradual Relaxation Technique with Application to Behavioral Synthesis, International Conference on Computer-Aided Design (ICCAD), Nov. 2003.
• [C1] J. Cong, Y. Fan, X. Yang, and Z. Zhang, Architecture and Synthesis for Multi-Cycle Communication, International Symposium on Physical Design (ISPD), Apr. 2003. (Invited Paper)