Deep Learning’s Impact on Computing:
Moore’s Law’s slowdown in recent years has coincided with deep learning’s insatiable demands for computing power. Training and inference for deep learning models require more computational power than traditional HPC applications.

Properties of Deep Learning Models:
Reduced precision (16-bit floating point) is sufficient for inference and even training. Many deep learning models involve a handful of specific operations, primarily linear algebra operations like matrix multiplies and vector dot products.

Custom Machine Learning Hardware:
Google has been building custom machine learning hardware, starting with an ASIC for inference. Inference is easier to handle than training, as a single chip can typically accommodate the computation needed for a model.

Training Systems:
Training systems require a more holistic approach than a single chip. The Tensor Processing Unit (TPU) version 2 is Google’s first training system. Each TPU chip features a large matrix multiply unit, scalar and vector units, two cores, and 16 gigabytes of HBM memory. TPUs support reduced precision in the multiplier units but allow 32-bit floating point operations in the rest of the chip.

TPU Pods:
TPU devices can be connected together into larger systems called pods. A pod consists of 64 TPU devices arranged in a 16 by 16 mesh with wraparound links. This configuration provides 11 and a half petaflops of compute, which is suitable for experimenting with various ideas. Pods can be sliced into subpods of different sizes to accommodate diverse user needs.

High-Level Programming Abstractions:
Google has focused on developing high-level programming abstractions in TensorFlow to facilitate easy performance optimization across different hardware platforms. The same TensorFlow program can run with minor modifications on CPUs, GPUs, and TPUs. Synchronous data parallelism is supported without modification on TPU pods, allowing for seamless scaling.

Cloud TPU Accelerators:
Google has been alpha testing Cloud TPUs with a select group of customers. Cloud TPUs are now available in beta as part of Google’s cloud products. Customers have reported significant speedups, with tasks that previously took days now taking hours.

Official Models for Cloud TPUs:
Google has released a set of official models that have been optimized for Cloud TPUs. These models converge to the desired accuracy levels and provide a compelling starting point for users. An experimental directory contains additional models that are still under development.

TensorFlow and XLA:
TensorFlow is a library for constructing and training machine learning models. XLA is an accelerated linear algebra system that serves as a compiler, runtime, and optimization tool for TensorFlow. XLA targets various systems and devices, including CPUs, GPUs, and TPUs.

TPU Optimizations:
XLA performs targeted independent optimizations, such as algebraic simplifications, on TensorFlow graphs. XLA’s backend generates target-specific code for the device being used, in this case, TPUs.

Cloud TPU Benefits:
Cloud TPUs offer significant speedups for training machine learning models. Internal search ranking model training time was reduced from 132 hours to 9 hours using a quarter pod of TPUs. Image models also benefit from similar speedups. WaveNet, a text-to-speech system, is used in production for inference on Cloud TPUs.

ResNet and Transformer Models:
ResNet can be trained on Cloud TPUs in under a day to achieve 76% accuracy on a single device. Scaling up to a half pod reduces the training time to 45 minutes. Cloud TPUs exhibit near-linear scaling in terms of images per second as the number of devices increases. Transformer models, which are useful for natural language and speech applications, are also supported by Cloud TPUs.

TPU Research Program:
Google is making 1,000 Cloud TPU devices available to researchers in machine learning. Researchers must commit to publishing their results openly and ideally open-sourcing their code. Researchers are expected to provide feedback on their experiences with TPUs, both positive and negative. The program aims to encourage more researchers to explore machine learning and its applications.

Challenges in Designing Machine Learning Hardware:
Uncertain future requirements for machine learning models and algorithms. Rapidly changing field with exponential growth in model complexity. Long development and deployment cycle for ASICs.

Research Topics for Machine Learning Hardware:
Low-precision training and inference. Sparsity and embeddings. Dynamic routing of very large models. Large embedding sizes for specific problems. Optimal batch size for different problems. Alternative optimization techniques to SGD.

Machine Learning Applications in Computer Systems:
High-performance machine learning models. Model parallelism for large models. Optimal placement of computations in large models across multiple devices. Reinforcement learning for placement optimization. Hierarchical grouping of nodes in graphs for placement.

Hardware Acceleration for Machine Learning:
Machine learning hardware customization offers exciting prospects for optimizing the performance of large-scale machine learning models. Specialization and focus on specific operations can unleash creativity in computer architecture, beyond traditional Moore’s law improvements.

Applying Machine Learning to Data Structures and Algorithms:
Research is exploring the use of machine learning to replace traditional algorithms and data structures. Learned models can approximate the behavior of B-trees, hash functions, Bloom filters, and more, with potential for improved performance and space utilization.

Machine Learning for Heuristic Optimization:
Many areas of computer systems, including compilers, networking, and operating systems, rely on heuristics for decision-making. Machine learning can be employed to learn and adapt these heuristics, resulting in improved performance and adaptability to specific usage patterns.

Opportunities for Machine Learning in Compiler Optimization:
Instruction scheduling, register allocation, loop nest parallelization, and other compiler optimizations can benefit from machine learning techniques.

Learning in Networking and Operating Systems:
Machine learning can be applied to network decisions like backoff strategies and window size adjustments, as well as operating system tasks such as buffer cache management and file system prefetching.

Challenges and Opportunities in Machine Learning Integration:
Key challenges include defining reward functions, integrating machine-learned models into low-level code, and maintaining low overhead for efficient decision-making.

Conclusion:
Machine learning hardware customization and integration into computer systems hold immense potential for enhancing performance, adaptability, and responsiveness.

Batch Size:
Contrary to traditional thinking, large batch sizes (32,000-64,000) are feasible for large data sets and models. Data parallelism can be used at a large scale, with a whole pod capable of handling substantial tasks.

Learning Rate Schedule:
A theoretical study suggests that instead of reducing the learning rate, increasing the batch size by the same factor can yield similar results. This approach leads to a regime where the early part of training, where learning happens rapidly, takes a larger portion of the training time.

Interconnect in TPUv2:
The design of the TPUv2’s interconnect received significant attention. Specific concerns and considerations related to the interconnect were not discussed in this segment.

Hardware Design for Machine Learning:
Jeff Dean emphasizes the need for a balanced system, with sufficient memory bandwidth, flops, and interconnect bandwidth to other chips. A 2D mesh structure is found to work well for data parallel applications, enabling efficient reductions and communication. High interconnect bandwidth facilitates tighter connectivity between floating point operations and enables effective use of model parallelism.

Machine Learning Hardware and Future Models:
Hardware choices for machine learning should consider the primitives that are fast and allow computation on problems of the desired size. Hardware designers try to predict which primitives will be most flexible for current and future algorithms. Small-scale experiments can help demonstrate the potential benefits of new approaches and inform hardware design decisions.

Generalization of Reinforcement Learning Policy:
The reinforcement learning (RL) model for placement is currently trained from scratch for each new model. Future work aims to train a single model that can place many different kinds of models, leveraging common subpieces to accelerate the placement process.

Insights for Programmers:
Programmers should not have to worry about placement details and should be provided with a higher level of abstraction. Automated placement techniques can optimize performance without requiring programmers to manage GPU resource allocation and parameter movement.