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IEEE International Conference on Solid-State Circuits (ISSCC)
2021 IEEE International Solid-State Circuits Conference (ISSCC)
13-22 Feb. 2021
Showing 1-25 of 269
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Solid- State Circuits Conference (ISSCC), 2021 IEEE International
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[ISSCC 2021 Front cover]
Year: 2021 | Volume: 64
[ISSCC 2021 Title Page]
Year: 2021 | Volume: 64
[ISSCC 2021 Copyright notice]
Year: 2021 | Volume: 64
ISSCC 2021 Table of Contents
Year: 2021 | Volume: 64
Remembrances of Dave Pricer
Year: 2021 | Volume: 64
Society's demand for more powerful and more energy-efficient computing is insatiable. Every day, more than half of the world's population (4.1 billion people) [1] read the news, communicate with friends and family, take online classes, shop for everything from groceries to furniture, manage their finances and personal health, using Internet, powered by semiconductor technologies. From vaccine and ...Show More
1.2 Adaptive Intelligence in The New Computing Era
Publication Year: 2021,Page(s):17 - 21
Cited by: Papers (8)
Connectivity is becoming ubiquitous with everything being connected all the time. Everything from streetlights to smart city cameras, automobiles, and appliances will be connected and generating continuous data. The unprecedented increase in the number of devices that generate data, most of which is unstructured, and the increase in the volume of data has led to a change in the way data is analyse...Show More
1.3 Working at the Intersection of Machine Learning, Signal Processing, Sensors, and Circuits
Publication Year: 2021,Page(s):26 - 29
Cited by: Papers (6)
Wireless technologies have propelled major advances in various industries including telecommunications, entertainment, and smart environments. Today, radios are embedded in cellphones, wearable devices, sensors, and control systems. Yet, in all of these diverse domains, radios are used for communications. Even when embedded in a sensor, the radio signals are used for communicating the sensed data,...Show More
There’s More to the Picture Than Meets the Eye, and in the future it will only become more so
Publication Year: 2021,Page(s):30 - 35
Cited by: Papers (2)
One of the fastest growing markets in the semiconductor industry is being driven by businesses in the solid-state imaging sector. An overview of the world-wide CIS (CMOS Image Sensor) market is illustrated in Figure 1.4.1. The actual CAGR (compound annual growth rate) from 2010 until 2019 was 15.2% in units and 16.9% in sales, while the forecasted CAGR from 2019 until 2024F is 11.5% in units and 7...Show More
ISSCC 2021 Awards
Year: 2021 | Volume: 64
Session 2 Overview: Highlighted Chip Releases: 5G and Radar Systems
Publication Year: 2021,Page(s):36 - 37
2.1 mm-Wave 5G Radios: Baseband to Waves
Ahmed Khalil;Islam Eshrah;Amr Elsherief;Ahmed Mehana;Mohamed Abdalla;Mohamed Mobarak;John Kilpatrick;Brian Hall;Ahmed Ashry;Hossam Fahmy;Sherif Salim;Russell Kernan;Brian Herdeg;Gary Sapia;Mohamed El-Nozahi;Mohamed Weheiba;Mark D’Amato;Christian Bautista;Kasey Chatzopoulos;Ahmed Ghoniem;Yossif Mosa;Daniel Roll;Kerem Ok
Publication Year: 2021,Page(s):38 - 40
Cited by: Papers (21)
There are many challenges in building millimeter-Wave (mmW) 5G radios [1] –[3]. Some of the key challenges are the cost, heat dissipation, and array calibration. This paper describes ADI’s full line-up of mmW 5G radios used today, with a focus on the millimeter wave front-end portion, and how it addresses some of these challenges. The radio block diagram, shown in Fig. 2.1.1, is an example of a du...Show More
2.1 mm-Wave 5G Radios: Baseband to Waves
Ahmed Khalil;Islam Eshrah;Amr Elsherief;Ahmed Mehana;Mohamed Abdalla;Mohamed Mobarak;John Kilpatrick;Brian Hall;Ahmed Ashry;Hossam Fahmy;Sherif Salim;Russell Kernan;Brian Herdeg;Gary Sapia;Mohamed El-Nozahi;Mohamed Weheiba;Mark D’Amato;Christian Bautista;Kasey Chatzopoulos;Ahmed Ghoniem;Yossif Mosa;Daniel Roll;Kerem Ok
Year: 2021 | Volume: 64
2.2 High-Performance and Small Form-Factor mm-Wave CMOS Radars for Automotive and Industrial Sensing in 76-to-81GHz and 57-to-64GHz Bands
Krishnanshu Dandu;Sreekiran Samala;Karan Bhatia;Meysam Moallem;Karthik Subburaj;Zeshan Ahmad;Daniel Breen;Sunhwan Jang;Tim Davis;Mayank Singh;Shankar Ram;Vashishth Dudhia;Marc DeWilde;Dheeraj Shetty;John Samuel;Zahir Parkar;Cathy Chi;Pilar Loya;Zachary Crawford;John Herrington;Ross Kulak;Abhinav Daga;Rakesh Raavi;Ravi Teja;Rajesh Veettil;Daniel Khemraj;Indu Prathapan;Prakash Narayanan;Naveen Narayanan;Sangamesh Anandwade;Jasbir Singh;Venkatesh Srinivasan;Neeraj Nayak;Karthik Ramasubramanian;Brian Ginsburg;Vijay Rentala
Publication Year: 2021,Page(s):39 - 41
Cited by: Papers (40)
Millimeterwave (mm-Wave) radar sensors operating in the 76-to-81 GHz band are a key component of advanced driver-assistance systems (ADAS) for enhanced automotive safety. The recent entry of CMOS solutions in this space has accelerated development of multi-mode Radars that can support long, medium and short-range applications [1–3]. As ADAS applications evolve to support higher levels of autonomy,...Show More
2.2 High-Performance and Small Form-Factor mm-Wave CMOS Radars for Automotive and Industrial Sensing in 76-to-81GHz and 57-to-64GHz Bands
Krishnanshu Dandu;Sreekiran Samala;Karan Bhatia;Meysam Moallem;Karthik Subburaj;Zeshan Ahmad;Daniel Breen;Sunhwan Jang;Tim Davis;Mayank Singh;Shankar Ram;Vashishth Dudhia;Marc DeWilde;Dheeraj Shetty;John Samuel;Zahir Parkar;Cathy Chi;Pilar Loya;Zachary Crawford;John Herrington;Ross Kulak;Abhinav Daga;Rakesh Raavi;Ravi Teja;Rajesh Veettil;Daniel Khemraj;Indu Prathapan;Prakash Narayanan;Naveen Narayanan;Sangamesh Anandwade;Jasbir Singh;Venkatesh Srinivasan;Neeraj Nayak;Karthik Ramasubramanian;Brian Ginsburg;Vijay Rentala
Year: 2021 | Volume: 64
2.3 SOLI: A Tiny Device for a New Human Machine Interface
Saverio Trotta;Dave Weber;Reinhard W. Jungmaier;Ashutosh Baheti;Jaime Lien;Dennis Noppeney;Maryam Tabesh;Christoph Rumpler;Michael Aichner;Siegfried Albel;Jagjit Singh Bal;Ivan Poupyrev
Publication Year: 2021,Page(s):42 - 44
Cited by: Papers (27)
With the introduction of the Internet of Things (IoT), there is an increasing focus on human-to-machine interaction. Nowadays, sensors make system and robots to see, hear, feel, and intuitively “understand” their surroundings. 60GHz radar [1] provides a very attractive solution for the sensing of human motion, enabling specific use cases such as: smart presence, hand gesture, and vital signs monit...Show More
Session 3 Overview: Highlighted Chip Releases: Modern Digital SoCs
Publication Year: 2021,Page(s):44 - 45
3.1 XBOX Series X: A Next-Generation Gaming Console SoC
Paul Paternoster;Andy Maki;Andres Hernandez;Mark Grossman;Michael Lau;David Sutherland;Aditya Mathad
Publication Year: 2021,Page(s):46 - 48
Cited by: Papers (1)
The XBOX Series X System-on-Chip (SoC) delivers an enormous improvement over the prior generation with up to 2× GPU performance, 3× CPU performance, 2.4x GPU performance/W, 1.7x memory bandwidth and 2× IO bandwidth to feed the additional processing capability and features shown in Fig. 3.1.1. The chip is fabricated in TSMC's N7 process node [6], containing 15.3B transistors on a 360.4mm2 die. It i...Show More
3.2 The A100 Datacenter GPU and Ampere Architecture
Jack Choquette;Edward Lee;Ronny Krashinsky;Vishnu Balan;Brucek Khailany
Publication Year: 2021,Page(s):48 - 50
Cited by: Papers (27)
The diversity of compute-intensive applications in modern cloud data centers has driven the explosion of GPU-accelerated cloud computing. Such applications include AI deep learning training and inference, data analytics, scientific computing, genomics, edge video analytics and 5G services, graphics rendering, and cloud gaming. The A100 GPU introduces several features targeting these workloads: a $...Show More
3.3 Kunlun: A 14nm High-Performance AI Processor for Diversified Workloads
Publication Year: 2021,Page(s):50 - 51
Cited by: Papers (11)
In order to be able to handle a wide range of AI applications, such as for speech, image, language and autonomous driving, it is necessary that an AI accelerator be flexible enough to handle diversified workloads. Baidu Kunlun, an AI chip designed in-house by Baidu, achieves this capability with high programmability, flexibility and performance. Baidu Kunlun was inspired by the XPU architecture [1...Show More
4.1 A 7nm 5G Mobile SoC Featuring a 3.0GHz Tri-Gear Application Processor Subsystem
Hsinchen Chen;Rolf Lagerquist;Ashish Nayak;Hugh Mair;Gokulakrishnan Manoharan;Ericbill Wang;Gordon Gammie;Efron Ho;Anand Rajagopalan;Lee-Kee Yong;Ramu Madhavaram;Madhur Jagota;Chi-Jui Chung;Sudhakar Maruthi;Jenny Wiedemeier;Tao Chen;Henry Hsieh;Daniel Dia;Amjad Sikiligiri;Manzur Rahman;Barry Chen;Curtis Lin;Vincent Lin;Elly Chiang;Cheng-Yuh Wu;Po-Yang Hsu;Jason Tsai;Wade Wu;Achuta Thippana;SA Huang
Publication Year: 2021,Page(s):54 - 56
Cited by: Papers (5)
This paper describes a new CPU subsystem featured in a 5G mobile SoC. The High-Performance (HP) core achieves a 3GHz clock frequency with full production yield across the fabrication range and operating environment. In contrast to previously published work [1], a third, balanced-performance (BP), gear is introduced which features a power-optimized implementation of the high-performance (HP) core. ...Show More
4.1 A 7nm 5G Mobile SoC Featuring a 3.0GHz Tri-Gear Application Processor Subsystem
Hsinchen Chen;Rolf Lagerquist;Ashish Nayak;Hugh Mair;Gokulakrishnan Manoharan;Ericbill Wang;Gordon Gammie;Efron Ho;Anand Rajagopalan;Lee-Kee Yong;Ramu Madhavaram;Madhur Jagota;Chi-Jui Chung;Sudhakar Maruthi;Jenny Wiedemeier;Tao Chen;Henry Hsieh;Daniel Dia;Amjad Sikiligiri;Manzur Rahman;Barry Chen;Curtis Lin;Vincent Lin;Elly Chiang;Cheng-Yuh Wu;Po-Yang Hsu;Jason Tsai;Wade Wu;Achuta Thippana;SA Huang
Year: 2021 | Volume: 64
4.2 A 12nm Autonomous-Driving Processor with 60.4TOPS, 13.8TOPS/W CNN Executed by Task-Separated ASIL D Control
Katsushige Matsubara;Lieske Hanno;Motoki Kimura;Atsushi Nakamura;Manabu Koike;Kazuaki Terashima;Shun Morikawa;Yoshihiko Hotta;Takahiro Irita;Seiji Mochizuki;Hiroyuki Hamasaki;Tatsuya Kamei
Publication Year: 2021,Page(s):56 - 58
Cited by: Papers (10)
Autonomous driving systems, when deployed to market, will require accurate and high-speed recognition, judgment and operation. Convolutional neural networks (CNNs) require large amounts of computation for pattern recognition. The CNN performance required for level-3 autonomous driving systems is 120TOPS or higher. As shown in Fig. 4.2.1, recent CNN implementations are oriented toward high performa...Show More
4.2 A 12nm Autonomous-Driving Processor with 60.4TOPS, 13.8TOPS/W CNN Executed by Task-Separated ASIL D Control
Katsushige Matsubara;Lieske Hanno;Motoki Kimura;Atsushi Nakamura;Manabu Koike;Kazuaki Terashima;Shun Morikawa;Yoshihiko Hotta;Takahiro Irita;Seiji Mochizuki;Hiroyuki Hamasaki;Tatsuya Kamei
Year: 2021 | Volume: 64
4.3 An Eight-Core 1.44GHz RISC-V Vector Machine in 16nm FinFET
Colin Schmidt;John Wright;Zhongkai Wang;Eric Chang;Albert Ou;Woorham Bae;Sean Huang;Anita Flynn;Brian Richards;Krste Asanović;Elad Alon;Borivoje Nikolić
Publication Year: 2021,Page(s):58 - 60
Cited by: Papers (9)
Modern workloads, such as deep neural networks (DNNs), increasingly rely on dense arithmetic compute patterns that are ill-suited for general-purpose processors, leading to a rise in domain-specific compute accelerators [1]. Many of these workloads can benefit from varying precision during computation, e.g. different precisions among layers and between training and inference for DNNs has been show...Show More
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Solid- State Circuits Conference (ISSCC), 2021 IEEE International
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