| DSPU: A 281.6mW Real-Time Deep Learning-Based Dense RGB-D Data Acquisition with Sensor Fusion and 3D Perception System-on-Chip
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Dongseok Im, Gwangtae Park, Zhiyong Li, Junha Ryu, Sanghoon Kang, Donghyeon Han, Jinsu Lee, Wonhoon Park, Hankyul Kwon and Hoi-Jun Yoo; KAIST |
| HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Mobile Devices
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Donghyeon Han, Dongseok Im, Gwangtae Park, Youngwoo Kim, Seokchan Song, Juhyoung Lee and Hoi-Jun Yoo; KAIST |
| VTA-NIC: Deep Learning Inference Serving in Network Interface Cards
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Kenji Tanaka, Yuki Arikawa, Kazutaka Morita, Tsuyoshi Ito, Takashi Uchida, Natsuko Saito, Shinya Kaji and Takeshi Sakamoto; NTT Device Technology Labs, NTT Corporation |
| A 13.7μJ/prediction 88% Accuracy CIFAR-10 Single-Chip Wired-logic Processor in 16-nm FPGA Using Non-Linear Neural Network
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Yao-Chung Hsu, Atsutake Kosuge, Rei Sumikawa, Kota Shiba, Mototsugu Hamada and Tadahiro Kuroda; The University of Tokyo |
| Neuro-CIM: A 310.4 TOPS/W Neuromorphic Computing-in-Memory Processor with Low WL/BL activity and Digital-Analog Mixed-mode Neuron Firing
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Sangyeob Kim, Sangjin Kim, Soyeon Um, Soyeon Kim, Kwantae Kim and Hoi-Jun Yoo; KAIST |
| System architecture and software stack for GDDR6-AiM, SK hynix’s very first GDDR6- based Accelerator-in-Memory (AiM)
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Yongkee Kwon, Kornijcuk Vladimir, Nahsung Kim, Woojae Shin, Jongsoon Won, Minkyu Lee, Hyunha Joo, Haerang Choi, Guhyun Kim, Byeongju An, Jeongbin Kim, Jaewook Lee, Ilkon Kim, Jaehan Park, Chanwook Park, Yosub Song, Byeongsu Yang, Hyungdeok Lee, Seho Kim, Daehan Kwon, Seongju Lee, Kyuyoung Kim, Sanghoon Oh, Joonhong Park, Gimoon Hong, Dongyoon Ka, Kyudong Hwang, Jeongje Park, Kyeongpil Kang, Jungyeon Kim, Junyeol Jeon, Myeongjun Lee, Minyoung Shin, Minhwan Shin, Jaekyung Cha, Changson Jung, Kijoon Chang, Chunseok Jeong, Euicheol Lim, Il Park and Junhyun Chun; SK hynix |
| Vision Perception Unit: Next-Generation Smart CMOS Image Sensor
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Wenqi Ji, Yubin Hu, Futang Wang, Yuze He, Xi Li, Jun Zhang, Yuxing Han and Jiangtao Wen; Tsinghua University |
| Large-scale Graph Neural Network Services through Computational SSD and In-Storage Processing Architectures
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Miryeong Kwon, Donghyun Gouk, Sangwon Lee and Myoungsoo Jung; KAIST |
| Trinity: End-to-End In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics
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Ji-Hoon Kim, Seunghee Han, Kwanghyun Park, Soo-Young Ji and Joo-Young Kim; KAIST |
| DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation
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Seongmin Hong, Seungjae Moon, Junsoo Kim, Sungjae Lee, Minsub Kim, Dongsoo Lee and Joo-Young Kim; KAIST |
| A 7-nm FinFET 1.2-TB/s/mm2 3D-Stacked SRAM with an Inductive Coupling Interface Using Over-SRAM Coils and Manchester-Encoded Synchronous Transceivers
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Kota Shiba, Mitsuji Okada, Atsutake Kosuge, Mototsugu Hamada and Tadahiro Kuroda; The University of Tokyo |
| LightTrader: World’s first AI-enabled High-Frequency Trading Solution with 16 TFLOPS / 64 TOPS Deep Learning Inference Accelerators
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Hyunsung Kim, Sungyeob Yoo, Jaewan Bae, Kyeongryeol Bong, Yoonho Boo, Karim Charfi, Hyo-Eun Kim, Hyun Suk Kim, Jinseok Kim, Byungjae Lee, Jaehwan Lee, Myeongbo Shim, Sungho Shin, Jeong Seok Woo, Joo-Young Kim, Sunghyun Park, and Jinwook Oh; Rebellions Inc. |
| Accelerating Graphic Rendering on Programmable RISC-V GPUs
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Blaise Tine, Varun Saxena, Santosh Srivatsan, Joshua R. Simpson, Fadi Alzammar, Liam Paul Cooper, Sam Jijina, Swetha Rajagoplan, Tejaswini Anand Kumar, Jeff Young and Hyesoon Kim; Georgia Tech |
| From High-Level Frameworks to custom Silicon with SODA
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Serena Curzel, Nicolas Bohm Agostini, Reece Neff, Ankur Limaye, Jeff (Jun) Zhang, Vinay Amatya, Marco Minutoli, Vito Giovanni Castellana, Joseph Manzano, David Brooks, Gu-Yeon Wei, Fabrizio Ferrandi and Antonino Tumeo; Politecnico di Milano |
| An Energy-efficient High-quality FHD Super-resolution Mobile Accelerator SoC with Hybrid-precision and Energy-efficient Cache Subsystem
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Zhiyong Li, Sangjin Kim, Dongseok Im, Donghyeon Han and Hoi-Jun Yoo; KAIST |
| Noema: Massive-Scale, Untethered, Real-Time Brain Activity Decoding
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Ameer Abdelhadi, Eugene Sha and Andreas Moshovos; University of Toronto |