7.1 Special Day on "Embedded AI": Industry AI chips

Time	Label	Presentation Title Authors
14:30	7.1.1	OPPORTUNITIES FOR ANALOG ACCELERATION OF DEEP LEARNING WITH PHASE CHANGE MEMORY Authors: Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, Hsinyu Tsai, Charles Mackin, Katherine Spoon, An Chen, Alexander Friz and Andrea Fasoli, IBM Research, US Abstract Storage Class Memory and High Bandwidth Memory Technologies are already reshaping systems architecture in interesting ways, by bringing cheap and high-density memory closer and closer to processing. Extrapolating on this trend, a new class of in-memory computing solutions is emerging, where some or all of the computing happens at the location of the data. Within the landscape of in-memory computing approaches, Non-von Neumann architectures seek to eliminate most of the data movement associated with computing, eliminating the demarcation between compute and memory. While such non-Von Neumann architectures could offer orders of magnitude performance improvements on certain workloads, they are not as general purpose nor as easily programmable as von-Neumann architectures. Therefore, well defined use cases need to exist to justify the hardware investment. Fortunately, acceleration of deep learning, which is both compute and memory-intensive, is one such use case. Today, the training of deep learning networks is done primarily in the cloud and could take days or weeks even when using many GPUs. Specialized hardware for training is thus primarily focused on speedup, with energy/power a secondary concern. On the other hand, 'Inference', the deployment and use of pre-trained models for real-world tasks, is done both in the cloud and on edge devices and presents hardware opportunities at both high speed and low power design points. In this presentation, we describe some of the opportunities and challenges in building accelerators for deep learning using analog volatile and non-volatile memory. We review our group's recent progress towards achieving software-equivalent accuracies on deep learning tasks in the presence of real-device imperfections such as non-linearity, asymmetry, variability and conductance drift. We will present some novel techniques and optimizations across device, circuit, and neural network design to achieve high accuracy with existing devices. We will then discuss challenges for peripheral circuit design and conclude by providing an outlook on the prospects for analog memory-based DNN accelerators.
14:52	7.1.2	EVENT-BASED AI FOR AUTOMOTIVE AND IOT Speaker: Etienne Pero, Prophesee, FR Author: Etienne Perot, Prophesee, FR Abstract Event cameras are a new type of sensor encoding visual information in the form of asynchronous events. An event corresponds to a change in the log-luminosity intensity at a given pixel location. Compared to standard frame cameras, event cameras have higher temporal resolution, higher dynamic range and lower power consumption. Thanks to these characteristics, event cameras find many applications in automotive and IoT, where low latency, robustness to challenging lighting conditions and low power consumption are critical requirements. In this talk we present recent advances in artificial intelligence applied to event cameras. In particular, we discuss how to adapt deep learning methods to work on events and their advantages compared to conventional frame-based methods. The presentation will be illustrated by results on object detection in automotive and IoT scenarios, running real-time on mobile platforms.
15:14	7.1.3	NEURONFLOW: A NEUROMORPHIC PROCESSOR ARCHITECTURE FOR LIVE AI APPLICATIONS Speaker: Orlando Moreira, GrAI Matter Labs, NL Authors: Orlando Moreira, Amirreza Yousefzadeh, Gokturk Cinserin, Rik-Jan Zwartenkot, Ajay Kapoor, Fabian Chersi, Peng Qiao, Peter Kievits, Mina Khoei, Louis Rouillard, Ashoka Visweswara and Jonathan Tapson, GrAI Matter Labs, NL Abstract This paper gives an overview of the Neuronflow many-core architecture. It is a neuromorphic data flow architecture that exploits brain-inspired concepts to deliver a scalable event-based processing engine for neuron networks in Live AI applications at the edge. Its design is inspired by brain biology, but not necessarily biologically plausible. The main design goal is the exploitation of sparsity to dramatically reduce latency and power consumption as required by sensor processing at the Edge. Download Paper (PDF; Only available from the DATE venue WiFi)
15:36	7.1.4	SPECK - SUB-MW SMART VISION SENSOR FOR MOBILE IOT APPLICATIONS Author: Ning Qiao, aiCTX, CH Abstract Speck is the first available neuromorphic smart vision sensor system-on-chip (SoC), which combines neuromorphic vision sensing and neuromorphic computation on a single die, for mW vision processing. The DVS pixel array is coupled directly to a new fully-asynchronous event-driven spiking CNN processor for highly compact and energy efficient dynamic visual processing. Speck supports a wide range of potential applications, spanning industrial and consumer-facing use cases.
16:00		End of session

Time

Label

Presentation Title
Authors

14:30

7.1.1

OPPORTUNITIES FOR ANALOG ACCELERATION OF DEEP LEARNING WITH PHASE CHANGE MEMORY
Authors:
Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, Hsinyu Tsai, Charles Mackin, Katherine Spoon, An Chen, Alexander Friz and Andrea Fasoli, IBM Research, US
Abstract
Storage Class Memory and High Bandwidth Memory Technologies are already reshaping systems architecture in interesting ways, by bringing cheap and high-density memory closer and closer to processing. Extrapolating on this trend, a new class of in-memory computing solutions is emerging, where some or all of the computing happens at the location of the data. Within the landscape of in-memory computing approaches, Non-von Neumann architectures seek to eliminate most of the data movement associated with computing, eliminating the demarcation between compute and memory. While such non-Von Neumann architectures could offer orders of magnitude performance improvements on certain workloads, they are not as general purpose nor as easily programmable as von-Neumann architectures. Therefore, well defined use cases need to exist to justify the hardware investment. Fortunately, acceleration of deep learning, which is both compute and memory-intensive, is one such use case. Today, the training of deep learning networks is done primarily in the cloud and could take days or weeks even when using many GPUs. Specialized hardware for training is thus primarily focused on speedup, with energy/power a secondary concern. On the other hand, 'Inference', the deployment and use of pre-trained models for real-world tasks, is done both in the cloud and on edge devices and presents hardware opportunities at both high speed and low power design points. In this presentation, we describe some of the opportunities and challenges in building accelerators for deep learning using analog volatile and non-volatile memory. We review our group's recent progress towards achieving software-equivalent accuracies on deep learning tasks in the presence of real-device imperfections such as non-linearity, asymmetry, variability and conductance drift. We will present some novel techniques and optimizations across device, circuit, and neural network design to achieve high accuracy with existing devices. We will then discuss challenges for peripheral circuit design and conclude by providing an outlook on the prospects for analog memory-based DNN accelerators.

14:52

7.1.2

EVENT-BASED AI FOR AUTOMOTIVE AND IOT
Speaker:
Etienne Pero, Prophesee, FR
Author:
Etienne Perot, Prophesee, FR
Abstract
Event cameras are a new type of sensor encoding visual information in the form of asynchronous events. An event corresponds to a change in the log-luminosity intensity at a given pixel location. Compared to standard frame cameras, event cameras have higher temporal resolution, higher dynamic range and lower power consumption. Thanks to these characteristics, event cameras find many applications in automotive and IoT, where low latency, robustness to challenging lighting conditions and low power consumption are critical requirements. In this talk we present recent advances in artificial intelligence applied to event cameras. In particular, we discuss how to adapt deep learning methods to work on events and their advantages compared to conventional frame-based methods. The presentation will be illustrated by results on object detection in automotive and IoT scenarios, running real-time on mobile platforms.

15:14

7.1.3

NEURONFLOW: A NEUROMORPHIC PROCESSOR ARCHITECTURE FOR LIVE AI APPLICATIONS
Speaker:
Orlando Moreira, GrAI Matter Labs, NL
Authors:
Orlando Moreira, Amirreza Yousefzadeh, Gokturk Cinserin, Rik-Jan Zwartenkot, Ajay Kapoor, Fabian Chersi, Peng Qiao, Peter Kievits, Mina Khoei, Louis Rouillard, Ashoka Visweswara and Jonathan Tapson, GrAI Matter Labs, NL
Abstract
This paper gives an overview of the Neuronflow many-core architecture. It is a neuromorphic data flow architecture that exploits brain-inspired concepts to deliver a scalable event-based processing engine for neuron networks in Live AI applications at the edge. Its design is inspired by brain biology, but not necessarily biologically plausible. The main design goal is the exploitation of sparsity to dramatically reduce latency and power consumption as required by sensor processing at the Edge.
Download Paper (PDF; Only available from the DATE venue WiFi)

15:36

7.1.4

SPECK - SUB-MW SMART VISION SENSOR FOR MOBILE IOT APPLICATIONS
Author:
Ning Qiao, aiCTX, CH
Abstract
Speck is the first available neuromorphic smart vision sensor system-on-chip (SoC), which combines neuromorphic vision sensing and neuromorphic computation on a single die, for mW vision processing. The DVS pixel array is coupled directly to a new fully-asynchronous event-driven spiking CNN processor for highly compact and energy efficient dynamic visual processing. Speck supports a wide range of potential applications, spanning industrial and consumer-facing use cases.

16:00

End of session