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ORCID36th Hot Chips Symposium 2024: Stanford, CA, USA
- 36th IEEE Hot Chips Symposium, HCS 2024, Stanford, CA, USA, August 25-27, 2024. IEEE 2024, ISBN 979-8-3503-8850-3
- Junha Ryu, Hankyul Kwon, Wonhoon Park, Zhiyong Li, Beomseok Kwon, Donghyeon Han, Dongseok Im, Sangyeob Kim, Hyungnam Joo, Minsung Kim, Hoi-Jun Yoo:
NeuGPU: A Neural Graphics Processing Unit for Instant Modeling and Real-Time Rendering on Mobile AR/VR Devices. 1 - Animesh Gupta, Japesh Vohra, Massimo Alioto:
CogniVision: A mW Power envelope SoC for Always-on Smart Vision in 40nm. 1 - Alan Smith, Vamsi Alla:
AMD Instinct MI300X Generative AI Accelerator and Platform Architecture. 1-22 - Annus Zulfiqar, Ali Imran, Venkat Kunaparaju, Ben Pfaff, Gianni Antichi, Muhammad Shahbaz:
A Smart Cache for a SmartNIC! Scaling End-Host Networking to 400Gbps and Beyond. 1 - Arik Gihon:
Lunar Lake Architecture Session. 1-49 - Tay-Jyi Lin, Ze Li, Yun-Cheng Chen, Chien-Tung Liu, Tien-Fu Chen, Jinn-Shyan Wang:
A 40-nm 13.88-TOPS/W FC-DNN Engine for 16-bit Intelligent Audio Processing Featuring Weight-Sharing and Approximate Computing. 1 - Jueun Jung, Seungbin Kim, Bokyoung Seo, Wuyoung Jang, Sangho Lee, Jeongmin Shin, Donghyeon Han, Kyuho Jason Lee:
LSPU: A 20.7 ms Low-Latency Point Neural Network-Based 3D Perception and Semantic LiDAR SLAM System-on-Chip for Autonomous Driving System. 1-28 - Khai-Duy Nguyen, Tuan-Kiet Dang, Binh Kieu-Do-Nguyen, Cong-Kha Pham, Trong-Thuc Hoang:
RISC-V-Based System-on-Chips for IoT Applications. 1 - Larry Tang, Siyuan Chen, Keshav Harisrikanth, Guanglin Xu, Franz Franchetti, Ken Mai:
A 1.19GHz 9.52Gsamples/sec Radix-8 FFT Hardware Accelerator in 28nm. 1 - Matthew Erler:
Sustainable Computing for AI & Cloud Native Workloads. 1-24 - Victor Peng:
The Journey to AI Pervasiveness. 1-36 - Kaifan Wang, Jian Chen, Yinan Xu, Zihao Yu, Zifei Zhang, Guokai Chen, Xuan Hu, Linjuan Zhang, Xi Chen, Wei He, Dan Tang, Ninghui Sun, Yungang Bao:
XiangShan: An Open-Source Project for High-Performance RISC-V Processors Meeting Industrial-Grade Standards. 1-25 - Eric Quinnell:
Tesla Transport Protocol Over Ethernet (TTPoE): A New Lossy, Exa-Scale Fabric for the Dojo AI Supercomputer. 1-23 - Kalhan Koul, Maxwell Strange, Jackson Melchert, Alex Carsello, Yuchen Mei, Olivia Hsu, Taeyoung Kong, Po-Han Chen, Huifeng Ke, Keyi Zhang, Qiaoyi Liu, Gedeon Nyengele, Akhilesh Balasingam, Jayashree Adivarahan, Ritvik Sharma, Zhouhua Xie, Christopher Torng, Joel S. Emer, Fredrik Kjolstad, Mark Horowitz, Priyanka Raina:
Onyx: A Programmable Accelerator for Sparse Tensor Algebra. 1-91 - Viansa Schmulbach, Jason Kim, Ethan Gao, Nikhil Jha, Ethan Wu, Oliver Yu, Ben Oliveau, Xiangwei Kong, Brendan Roberts, Connor McMahon, Lixiang Yin, Vamber Yang, Brendan Brenner, George Moujaes, Boyu Hao, Lucy Revina, Kevin Anderson, Bryan Ngo, Yufeng Chi, Hongyi Huang, Reza Sajadiany, Raghav Gupta, Ella Schwarz, Jennifer Zhou, Ken Ho, Jerry Zhao, Anita Flynn, Borivoje Nikolic:
NeCTAr and RASoC: Tale of Two Class SoCs for Language Model Interference and Robotics in Intel 16. 1 - Saeed Fathololoumi:
4 Tb/s Optical Compute Interconnect Chiplet for XPU-to-XPU Connectivity. 1-18 - Shrijeet Mukherjee, Thomas Norrie:
ACF-S: An 8-Terabit / Sec SuperNIC for High-Performance Data Movement in AI & Accelerated Compute Networks. 1-25 - Mahesh Maddury, Pankaj Kansal, Olívia Wu:
Next Gen MTIA -Recommendation Inference Accelerator. 1-27 - Manish Mehta:
An AI Compute ASIC with Optical Attach to Enable Next Generation Scale-Up Architectures. 1-30 - Tomai Knopp, Jeffrey Chu, Sagheer Ahmad:
AMD Versal™ AI Edge Series Gen 2 for Vision and Automotive. 1-28 - Seokchan Song, Haoyang Sang, Dongseok Im, Donghyeon Han, Sangyeob Kim, Hongseok Lee, Hoi-Jun Yoo:
Space-Mate: A 303.5mW Real-Time NeRF SLAM Processor with Sparse-Mixture-of-Experts-based Acceleration. 1 - Chris Berry:
IBM Telum® II Processor and IBM Spyre™ Accelerator Chip for AI. 1-29 - Sean Lie:
Wafer-Scale AI: GPU Impossible Performance. 1-71 - Praveen Mosur:
Built for the Edge: The Intel® Xeon® 6 SoC. 1-28 - Jasmina Vasiljevic, Davor Capalija:
Blackhole & TT-Metalium: The Standalone AI Computer and its Programming Model. 1-30 - Ajay Tirumala, Raymond Wong:
NVIDIA Blackwell Platform: Advancing Generative AI and Accelerated Computing. 1-33 - Vishnu P. Nambiar, Yi Sheng Chong, Thilini Kaushalya Bandara, Dhananjaya Wijerathne, Zhaoying Li, Rohan Juneja, Li-Shiuan Peh, Tulika Mitra, Anh Tuan Do:
PACE: A Scalable and Energy Efficient CGRA in a RISC-V SoC for Edge Computing Applications. 1 - Brad Cohen, Mahesh Subramony, Mike Clark:
Next Generation "Zen 5" Core. 1-27 - Binh Kieu-Do-Nguyen, Khai-Duy Nguyen, Tuan-Kiet Dang, Cong-Kha Pham, Trong-Thuc Hoang:
A Trusted Execution Environment RISC-V System on Chip. 1 - Raghu Prabhakar:
SambaNova SN40L RDU: Breaking the Barrier of Trillion+ Parameter Scale Gen AI Computing. 1-24 - June Paik:
RNGD - Tensor Contraction Processor for Sustainable AI Computing. 1-33 - Gerard Williams:
Qualcomm Oryon™ CPU. 1-21 - Sherry Xu, Chandru Ramakrishnan:
Inside Maia 100. 1-17 - Sungyeob Yoo, Geonwoo Ko, Seri Ham, Seeyeon Kim, Yi Chen, Joo-Young Kim:
Picasso: An Area/Energy-Efficient End-to-End Diffusion Accelerator with Hyper-Precision Data Type. 1-15 - Roman Kaplan:
Intel Gaudi 3 AI Accelerator: Architected for Gen AI Training and Inference. 1-16 - Blaise Tine, Hyesoon Kim:
Towards "True" GPU Performance Scaling for OpenGPU. 1-10 - Jun Makino:
MN-Core 2: Second-Generation Processor of MN-Core Architecture for AI and General-Purpose HPC Application. 1-22 - Trevor Cai:
Predictable Scaling and Infrastructure. 1-27 - Sangyeob Kim, Sangjin Kim, Wooyoung Jo, Soyeon Kim, Seongyon Hong, Nayeong Lee, Hoi-Jun Yoo:
A Low-Power Large-Language-Model Processor with Big-Little Network and Implicit-Weight-Generation for On-Device AI. 1 - Guhyun Kim, Jinkwon Kim, Nahsung Kim, Woojae Shin, Jongsoon Won, Hyunha Joo, Haerang Choi, Byeongju An, Gyeongcheol Shin, Dayeon Yun, Jeongbin Kim, Changhyun Kim, Ilkon Kim, Jaehan Park, Yosub Song, Byeongsu Yang, Hyeongdeok Lee, Seungyeong Park, Wonjun Lee, Seonghun Kim, Yonghoon Park, Yousub Jung, Gi-Ho Park, Euicheol Lim:
SK Hynix AI-Specific Computing Memory Solution: From AiM Device to Heterogeneous AiMX-xPU System for Comprehensive LLM Inference. 1-26
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