loop nest describe how to break the DNN workload into smaller pieces, referred to as tiling/blocking to account for the limited storage capacity at different levels of the memory hierarchy.
• Chapter 6 presents the process of mapping a DNN workload on to a DNN accelerator. It describes the steps required to find an optimized mapping, including enumerating all legal mappings and searching those mappings by employing models that project throughput and energy efficiency.
The third module discusses how additional improvements in efficiency can be achieved either by moving up the stack through the co-design of the algorithms and hardware or down the stack by using mixed signal circuits and new memory or device technology. In the cases where the algorithm is modified, the impact on accuracy must be carefully evaluated.
• Chapter 7 describes how reducing the precision of data and computation can result in increased throughput and energy efficiency. It discusses how to reduce precision using quantization and the associated design considerations, including hardware cost and impact on accuracy.
• Chapter 8 describes how exploiting sparsity in DNNs can be used to reduce the footprint of the data, which provides an opportunity to reduce storage requirements, data movement, and arithmetic operations. It describes various sources of sparsity and techniques to increase sparsity. It then discusses how sparse DNN accelerators can translate sparsity into improvements in energy-efficiency and throughput. It also presents a new abstract data representation that can be used to express and obtain insight about the dataflows for a variety of sparse DNN accelerators.
• Chapter 9 describes how to optimize the structure of the DNN models (i.e., the ‘network architecture’ of the DNN) to improve both throughput and energy efficiency while trying to minimize impact on accuracy. It discusses both manual design approaches as well as automatic design approaches (i.e., neural architecture search).
• Chapter 10, on advanced technologies, discusses how mixed-signal circuits and new memory technologies can be used to bring the compute closer to the data (e.g., processing in memory) to address the expensive data movement that dominates throughput and energy consumption of DNNs. It also briefly discusses the promise of reducing energy consumption and increasing throughput by performing the computation and communication in the optical domain.
What’s New?
This book is an extension of a tutorial paper written by the same authors entitled “Efficient Processing of Deep Neural Networks: A Tutorial and Survey” that appeared in the Proceedings of the IEEE in 2017 and slides from short courses given at ISCA and MICRO in 2016, 2017, and 2019 (slides available at http://eyeriss.mit.edu/tutorial.html). This book includes recent works since the publication of the tutorial paper along with a more in-depth treatment of topics such as dataflow, mapping, and processing in memory. We also provide updates on the fast-moving field of co-design of DNN models and hardware in the areas of reduced precision, sparsity, and efficient DNN model design. As part of this effort, we present a new way of thinking about sparse representations and give a detailed treatment of how to handle and exploit sparsity. Finally, we touch upon recurrent neural networks, auto encoders, and transformers, which we did not discuss in the tutorial paper.
Scope of book
The main goal of this book is to teach the reader how to tackle the computational challenge of efficiently processing DNNs rather than how to design DNNs for increased accuracy. As a result, this book does not cover training (only touching on it lightly), nor does it cover the theory of deep learning or how to design DNN models (though it discusses how to make them efficient) or use them for different applications. For these aspects, please refer to other references such as Goodfellow’s book [2], Amazon’s book [3], and Stanford cs231n course notes [4].
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer
June 2020
Acknowledgments
The authors would like to thank Margaret Martonosi for her persistent encouragement to write this book. We would also like to thank Liane Bernstein, Davis Blalock, Natalie Enright Jerger, Jose Javier Gonzalez Ortiz, Fred Kjolstad, Yi-Lun Liao, Andreas Moshovos, Boris Murmann, James Noraky, Angshuman Parashar, Michael Pellauer, Clément Pit-Claudel, Sophia Shao, Mahmhut Ersin Sinangil, Po-An Tsai, Marian Verhelst, Tom Wenisch, Diana Wofk, Nellie Wu, and students in our “Hardware Architectures for Deep Learning” class at MIT, who have provided invaluable feedback and discussions on the topics described in this book. We would also like to express our deepest appreciation to Robin Emer for her suggestions, support, and tremendous patience during the writing of this book.
As mentioned earlier in the Preface, this book is an extension of an earlier tutorial paper, which was based on tutorials we gave at ISCA and MICRO. We would like to thank David Brooks for encouraging us to do the first tutorial at MICRO in 2016, which sparked the effort that led to this book.
This work was funded in part by DARPA YFA, the DARPA contract HR0011-18-3-0007, the MIT Center for Integrated Circuits and Systems (CICS), the MIT-IBM Watson AI Lab, the MIT Quest for Intelligence, the NSF E2CDA 1639921, and gifts/faculty awards from Nvidia, Facebook, Google, Intel, and Qualcomm.
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer
June 2020
PART I
Understanding Deep Neural Networks
CHAPTER 1
Introduction
Deep neural networks (DNNs) are currently the foundation for many modern artificial intelligence (AI) applications [5]. Since the breakthrough application of DNNs to speech recognition [6] and image recognition1 [7], the number of applications that use DNNs has exploded. These DNNs are employed in a myriad of applications from self-driving cars [8], to detecting cancer [9], to playing complex games [10]. In many of these domains, DNNs are now able to exceed human accuracy. The superior accuracy of DNNs comes from their ability to extract high-level features from raw sensory data by using statistical learning on a large amount of data to obtain an effective representation of an input space. This is different from earlier approaches that use hand-crafted features or rules designed by experts.
The superior accuracy of DNNs, however, comes at the cost of high computational complexity. To date, general-purpose compute engines, especially graphics processing units (GPUs), have been the mainstay for much DNN processing. Increasingly, however, in these waning days of Moore’s law, there is a recognition that more specialized hardware is needed to keep improving compute performance and energy efficiency [11]. This is especially true in the domain of DNN computations. This book aims to provide an overview of DNNs, the various tools for understanding their behavior, and the techniques being explored to efficiently accelerate their computation.
1.1 BACKGROUND ON DEEP NEURAL NETWORKS
In this section, we describe the position of DNNs in the context of artificial intelligence (AI) in general and some
