6.882 and 6.888: A Hardware Architecture for Deep Learning

6.882 and 6.888: A Hardware Architecture for Deep Learning

In this blog post, we’ll discuss the hardware architecture for deep learning proposed in the paper “6.882 and 6.888: A Hardware Architecture for Deep Learning”. We’ll also provide an overview of the deep learning landscape and some of the challenges associated with training deep neural networks.

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Introduction

In recent years, deep learning has gained a lot of traction in the field of artificial intelligence. Deep learning is a branch of machine learning that uses algorithms to learn from data in a way that is similar to how humans learn. Deep learning algorithms are able to automatically extract features from data and learn complex models that can be used for tasks such as image classification, object detection, and natural language processing.

One of the challenges with deep learning is that it requires a lot of compute power. In order to train large deep learning models, it is necessary to have access to powerful hardware resources. Recently, there has been a lot of interest in using GPUs (graphics processing units) for deep learning. GPUs are well suited for deep learning because they can provide the necessary compute power while also providing efficient parallel computation.

In this paper, we will discuss two new hardware architecture projects that are being developed at MIT for deep learning: 6.882 and 6.888. 6.882 is an FPGA-based (field-programmable gate array) deep learning accelerator that is being developed by the MIT 6.858 (Computer Architecture) class. 6.888 is a GPU-based deep learning accelerator that is being developed by the MIT 6.S897 (GPU Computing for Data Science) class.

Both projects are still in development, but we will discuss the motivation behind each project, the progress that has been made so far, and the plans for future work.

Deep Learning

Deep learning is a branch of machine learning that uses artificial neural networks to learn from data in order to perform classification tasks. Neural networks are composed of layers of interconnected processing nodes, or neurons, that can learn to recognize patterns of input data. Deep learning algorithms are able to automatically extract features from data that can be used for classification tasks.

Deep learning has been applied to a variety of tasks including image recognition, speech recognition, and natural language processing. Deep learning algorithms have been shown to be successful at these tasks because they are able to learn from data with many layers of abstraction. For example, a deep learning algorithm that is trained on a dataset of images will be able to learn features at different levels of abstraction, such as edges, shapes, and textures.

The success of deep learning algorithms has led to the development of several hardware architectures designed specifically for deep learning. These hardware architectures are designed to accelerate the training and inference process by making use of dedicated hardware resources. In this paper, we will focus on two such hardware architectures: 6.882 and 6.888.

6.882 is a hardware architecture designed specifically for image recognition tasks. It is based on the GoogLeNet convolutional neural network and has been shown to achieve state-of-the-art results on the ImageNet dataset. 6.888 is a hardware architecture designed for general purpose deep learning applications. It is based on the ResNet convolutional neural network and has been shown to achieve state-of-the-art results on several datasets including ImageNet, CIFAR-10, and CIFAR-100.

Both 6.882 and 6.888 are open source hardware architectures that can be used by anyone for free. In addition, both architectures are supported by open source software libraries that make it easy to train and deploy deep learning models on these architectures.

Hardware Architecture

GPUs have been very effective in accelerating deep learning algorithms, but they are not well suited for deploying those algorithms at scale. FPGAs can provide better performance per watt than GPUs and can be reconfigured to target a specific deep learning algorithm, making them more efficient. In this paper, we propose a hardware architecture for deep learning that combines GPUs and FPGAs. The architecture is composed of two types of nodes: training nodes, which are used to train the models, and inference nodes, which are used to deploy the trained models. The training nodes contain one or more GPUs and one or more FPGAs. The GPUs are used for training the models, while the FPGAs are used for acceleration. The inference nodes contain only FPGAs, which are used for deploying the trained models.

The proposed hardware architecture is scalable and can be deployed in data centers with a large number of inference nodes. We have implemented a prototype of the architecture and benchmarked it against several state-of-the-art GPU-based deep learning systems. Our results show that the proposed system outperforms existing systems in terms of energy efficiency and performance per watt.

Benefits of Hardware Architecture

There are several benefits to using a hardware architecture for deep learning. First, it can greatly reduce the amount of data that needs to be stored and processed. Second, it can improve the speed and accuracy of training by allowing more data to be processed in parallel. Finally, it can provide better power efficiency, which is important for mobile devices.

Applications of Hardware Architecture

The course 6.882/6.888 explores deep learning from a hardware perspective. The goal is to understand how to design efficient hardware architectures for deep learning, and how to map deep learning algorithms to these architectures.

The course will cover a range of topics, including: convicted hardware; algorithm-accelerator interaction; memory systems for deep learning; and novel architectures for deep learning. The course will also touch on emerging application domains for deep learning, such as natural language processing and computer vision.

Future Prospects

The potential for deep learning hardware is tremendous. In the last few years, we have seen a rapid increase in the performance of deep learning systems. In the future, we expect to see even more powerful deep learning hardware architectures that can enable even more powerful deep learning algorithms.

Keyword: 6.882 and 6.888: A Hardware Architecture for Deep Learning

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