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ORCID34th Hot Chips Symposium 2022: Cupertino, CA, USA
- 2022 IEEE Hot Chips 34 Symposium, HCS 2022, Cupertino, CA, USA, August 21-23, 2022. IEEE 2022, ISBN 978-1-6654-6028-6
- Alan Smith, Norman James:
AMD Instinct™ MI200 Series Accelerator and Node Architectures. 1-23 - Sean Lie:
Cerebras Architecture Deep Dive: First Look Inside the HW/SW Co-Design for Deep Learning : Cerebras Systems. 1-34 - Alexander Ishii, Ryan Wells:
The Nvlink-Network Switch: Nvidia's Switch Chip for High Communication-Bandwidth Superpods. 1-23 - Mediatek Ericbill Wang, Arm Stefan Rosinger, Saurabh Pradhan:
Dimensity 9000 - A Flagship Smartphone SoC. 1-23 - Sangyeob Kim, Sangjin Kim, Soyeon Um, Soyeon Kim, Kwantae Kim, Hoi-Jun Yoo:
Neuro-CIM: A 310.4 TOPS/W Neuromorphic Computing-in-Memory Processor with Low WL/BL activity and Digital-Analog Mixed-mode Neuron Firing. 1-25 - Wilfred Gomes, Slade Morgan, Boyd Phelps, Tim Wilson, Erik Hallnor:
Meteor Lake and Arrow Lake Intel Next-Gen 3D Client Architecture Platform with Foveros. 1-40 - Emil Talpes, Douglas Williams, Debjit Das Sarma:
DOJO: The Microarchitecture of Tesla's Exa-Scale Computer. 1-28 - Jaideep Dastidar, David Riddoch, Jason Moore, Steve Pope, Jim Wesselkamper:
AMD 400G Adaptive SmartNIC SoC: Technology preview. 1-31 - Richard Grisenthwaite:
Arm Morello Evaluation Platform -Validating CHERI-based Security in a High-performance System. 1-22 - Jack Choquette:
Nvidia Hopper GPU: Scaling Performance. 1-46 - Serena Curzel
, Nicolas Bohm Agostini, Reece Neff, Ankur Limaye, Jeff Jun Zhang, Vinay Amatya, Marco Minutoli, Vito Giovanni Castellana, Joseph B. Manzano, David Brooks, Gu-Yeon Wei, Fabrizio Ferrandi, Antonino Tumeo:
From High-Level Frameworks to custom Silicon with SODA. 1-13 - Jonathon Evans:
Nvidia Grace. 1-20 - Yao-Chung Hsu, Atsutake Kosuge, Rei Sumikawa, Kota Shiba, Mototsugu Hamada, Tadahiro Kuroda:
A 13.7μJ/prediction 88% Accuracy CIFAR-10 Single-Chip Wired-logic Processor in 16-nm FPGA using Non-Linear Neural Network. 1-14 - Ji-Hoon Kim, Seunghee Han, Kwanghyun Park, Soo-Young Ji, Joo-Young Kim:
Trinity: End-to-End In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics. 1-16 - Ameer Abdelhadi, Eugene Sha, Andreas Moshovos:
A Massive-Scale Brain Activity Decoding Chip. 1-65 - Mike Hong, Lingjie Xu:
壁仞™ BR100 GPGPU: Accelerating Datacenter Scale AI Computing. 1-22 - Dongseok Im, Gwangtae Park, Zhiyong Li, Junha Ryu, Sanghoon Kang, Donghyeon Han, Jinsu Lee, Wonhoon Park, Hankyul Kwon, Hoi-Jun Yoo:
DSPU: A 281.6mW Real-Time Deep Learning-Based Dense RGB-D Data Acquisition with Sensor Fusion and 3D Perception System-on-Chip. 1-25 - Blaise Tine, Varun Saxena, Santosh Srivatsan, Joshua R. Simpson, Fadi Alzammar, Liam Paul Cooper, Sam Jijina, Swetha Rajagoplan, Tejaswini Anand Kumar, Jeffrey Young, Hyesoon Kim:
Accelerating Graphic Rendering on Programmable RISC-V GPUs. 1-15 - Michael Ditty:
NVIDIA ORIN System-On-Chip. 1-17 - Zhiyong Li, Sangjin Kim, Dongseok Im, Donghyeon Han, Hoi-Jun Yoo:
An Efficient High-quality FHD Super-resolution Mobile Accelerator SoC with Hybrid-precision and Energy-efficient Cache. 1-26 - Abhishek Bhattacharjee, Rajit Manohar:
HALO: A Flexible and Low Power Processing Fabric for Brain-Computer Interfaces. 1-37 - Praveen Mosur:
Built for the Edge: The Next-Generation Intel® Xeon D 2700 & 1700 processors. 1-15 - Pat Gelsinger:
Semiconductors Run the World : Hot Chips 2022. 1-19 - Sergey Y. Shumarayev, Allen Chan, Tim Hoang, Robert Keller:
Heterogenous Integration Enables FPGA Based Hardware Acceleration for RF Applications. 1-20 - Kathleen Feng, Alex Carsello, Taeyoung Kong, Kalhan Koul, Qiaoyi Liu, Jackson Melchert, Gedeon Nyengele, Maxwell Strange, Keyi Zhang, Ankita Nayak, Jeff Setter, James Thomas, Kavya Sreedhar, Po-Han Chen, Nikhil Bhagdikar, Zachary Myers, Brandon D'Agostino, Pranil Joshi, Stephen Richardson, Rick Bahr, Christopher Torng, Mark Horowitz, Priyanka Raina:
Amber: Coarse-Grained Reconfigurable Array-Based SoC for Dense Linear Algebra Acceleration. 1-30 - Kenji Tanaka, Yuki Arikawa, Kazutaka Morita, Tsuyoshi Ito, Takashi Uchida, Natsuko Saito, Shinya Kaji, Takeshi Sakamoto:
VTA-NIC: Deep Learning Inference Serving in Network Interface Cards. 1-16 - Robert Beachler, Martin Snelgrove:
Untether Ai : Boqueria. 1-19 - 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, Jinwook Oh:
LightTrader : World's first AI-enabled High-Frequency Trading Solution with 16 TFLOPS / 64 TOPS Deep Learning Inference Accelerators. 1-10 - Christoph Schulien:
Enabling Scalable Application-Specific Optical Engines (ASOE) by Monolithic Integration of Photonics and Electronics. 1-32 - Alfio Di Mauro, Moritz Scherer, Davide Rossi, Luca Benini:
Kraken: A Direct Event/Frame-Based Multi-sensor Fusion SoC for Ultra-Efficient Visual Processing in Nano-UAVs. 1-19 - Kota Shiba, Mitsuji Okada, Atsutake Kosuge, Mototsugu Hamada, Tadahiro Kuroda:
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. 1-14 - Miryeong Kwon, Donghyun Gouk, Sangwon Lee, Myoungsoo Jung:
Large-scale Graph Neural Network Services through Computational SSD and In-Storage Processing Architectures. 1-25 - Donghyeon Han, Dongseok Im, Gwangtae Park, Youngwoo Kim, Seokchan Song, Juhyoung Lee, Hoi-Jun Yoo:
HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Mobile Devices. 1-18 - Bill Chang, Rajiv Kurian, Doug Williams, Eric Quinnell:
DOJO: Super-Compute System Scaling for ML Training. 1-45 - Seongmin Hong, Seungjae Moon, Junsoo Kim, Sungjae Lee, Minsub Kim, Dongsoo Lee, Joo-Young Kim:
DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation. 1-17 - Wenqi Ji, Yuxing Han, Jiangtao Wen, Yubin Hu, Futang Wang, Yuze He, Xi Li, Jun Zhang:
Vision Perception Unit: Next-Generation Smart CMOS Image Sensor. 1-13 - 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, Seong Ju Lee, Kyuyoung Kim, Sanghoon Oh, Joonhong Park, Gimoon Hong, Dongyoon Ka, Kyudong Hwang, Jeongje Park, Kyeong Pil Kang, Jungyeon Kim, Junyeol Jeon, Myeongjun Lee, Minyoung Shin, Minhwan Shin, Jaekyung Cha, Changson Jung, Kijoon Chang, Chunseok Jeong, Euicheol Lim, Il Park, Junhyun Chun:
System Architecture and Software Stack for GDDR6-AiM. 1-25 - Dennis Abts, John Kim, Garrin Kimmell, Matthew Boyd, Kris Kang, Sahil Parmar, Andrew C. Ling, Andrew Bitar, Ibrahim Ahmed, Jonathan Ross:
The Groq Software-defined Scale-out Tensor Streaming Multiprocessor : From chips-to-systems architectural overview. 1-69 - Hong Jiang:
Intel's Ponte Vecchio GPU : Architecture, Systems & Software. 1-29 - Jim Gibney:
AMD Ryzen™ 6000 Series for Mobile : Technology Overview. 1-24 - Sung Joo Park, H. Kim, K.-S. Kim, J. So, J. Ahn, W.-J. Lee, D. Kim, Young-Ju Kim, J. Seok, J.-G. Lee, H.-Y. Ryu, C. Y. Lee, J. Prout, K.-C. Ryoo, S.-J. Han, M.-K. Kook, J. S. Choi, J. Gim, Y. S. Ki, S. Ryu, C. Park, D.-G. Lee, J. Cho, H. Song, Jin-Yup Lee:
Scaling of Memory Performance and Capacity with CXL Memory Expander. 1-27
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