SIEPON conformance testing and certification. - Lowell D. Lamb; Yang Lu; Qian Liu; Ravi Subramaniam; Toshihiko Kusano; Masashi Tadokoro; Motoyuki Takizawa (IEEE Commun. Mag., 2012)

ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems. - Shuo Huai; Lei Zhang 072; Di Liu 002; Weichen Liu; Ravi Subramaniam (DAC, 2021)

HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs. - Hao Kong; Di Liu 002; Xiangzhong Luo; Weichen Liu; Ravi Subramaniam (ASP-DAC, 2022)

Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hardware. - Hao Kong; Di Liu 002; Shuo Huai; Xiangzhong Luo; Weichen Liu; Ravi Subramaniam; Christian Makaya; Qian Lin 001 (ICCAD, 2022)

Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems. - Shuo Huai; Di Liu 002; Hao Kong; Xiangzhong Luo; Weichen Liu; Ravi Subramaniam; Christian Makaya; Qian Lin 001 (ICCD, 2022)

EDLAB: A Benchmark for Edge Deep Learning Accelerators. - Hao Kong; Shuo Huai; Di Liu 002; Lei Zhang 072; Hui Chen 016; Shien Zhu; Shiqing Li; Weichen Liu; Manu Rastogi; Ravi Subramaniam; Madhu Athreya; M. Anthony Lewis (IEEE Des. Test, 2022)

Bringing AI to edge: From deep learning's perspective. - Di Liu 002; Hao Kong; Xiangzhong Luo; Weichen Liu; Ravi Subramaniam (Neurocomputing, 2022)

Crossbar-Aligned & Integer-Only Neural Network Compression for Efficient in-Memory Acceleration. - Shuo Huai; Di Liu 002; Xiangzhong Luo; Hui Chen 016; Weichen Liu; Ravi Subramaniam (ASP-DAC, 2023)

Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning. - Hao Kong; Di Liu 002; Xiangzhong Luo; Shuo Huai; Ravi Subramaniam; Christian Makaya; Qian Lin 001; Weichen Liu (DAC, 2023)

EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI. - Hao Kong; Xiangzhong Luo; Shuo Huai; Di Liu 002; Ravi Subramaniam; Christian Makaya; Qian Lin 001; Weichen Liu (DATE, 2023)

Latency-constrained DNN architecture learning for edge systems using zerorized batch normalization. - Shuo Huai; Di Liu 002; Hao Kong; Weichen Liu; Ravi Subramaniam; Christian Makaya; Qian Lin 001 (Future Gener. Comput. Syst., 2023)

CRIMP: Compact & Reliable DNN Inference on In-Memory Processing via Crossbar-Aligned Compression and Non-ideality Adaptation. - Shuo Huai; Hao Kong; Xiangzhong Luo; Shiqing Li; Ravi Subramaniam; Christian Makaya; Qian Lin 001; Weichen Liu (ACM Trans. Embed. Comput. Syst., 2023)

On Hardware-Aware Design and Optimization of Edge Intelligence. - Shuo Huai; Hao Kong; Xiangzhong Luo; Di Liu 002; Ravi Subramaniam; Christian Makaya; Qian Lin 001; Weichen Liu (IEEE Des. Test, 2023)

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI. - Hao Kong; Di Liu 002; Shuo Huai; Xiangzhong Luo; Ravi Subramaniam; Christian Makaya; Qian Lin 001; Weichen Liu (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)

FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection. - Vikash Sathiamoorthy; Shuo Huai; Hao Kong; Di Liu 002; Wendy Yong Yi Loy; Christian Makaya; Daren Ho; Ravi Subramaniam; Qian Lin 001; Weichen Liu (ACM Great Lakes Symposium on VLSI, 2024)

EvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems. - Shuo Huai; Hao Kong; Shiqing Li; Xiangzhong Luo; Ravi Subramaniam; Christian Makaya; Qian Lin 001; Weichen Liu (IEEE Embed. Syst. Lett., 2024)