Designing a deep neural network accelerator is a multi-objective optimization problem maximizing accuracy and minimizing energy consumption. The design space can easily have millions of possible configurations and scales exponentially. We propose using Bayesian optimization to intelligently sample the design space.
Compared to a standard search solutions, Bayesian optimization:. Fathom is a collection of eight archetypal deep learning workloads to enable broad, realistic architecture research. Each model is derived from a seminal work in the deep learning community, ranging from the convolutional neural network of Krizhevsky et al.
We use a set of application-level modeling tools built around the TensorFlow deep learning framework in order to analyze the fundamental performance characteristics of each model.
We breakdown where time is spent, computational similarities between the models, differences in inference and training, and the effects of parallelism on scaling. International Symposium Workload Characterization, While published accelerators easily give an order of magnitude improvement over general-purpose hardware, few studies look beyond an initial implementation.
Minerva is an automated co-design approach to optimizing DNN accelerators which goes further.
Compared to a fixed-point accelerator baseline, we show that fine-grained, data-type optimization reduces power by 1. Across five datasets, these optimizations provide a collective average of 8. International Symposium on Computer Architecture, Deep learning algorithms present an exciting opportunity for efficient VLSI implementations due to several useful properties: 1 an embarrassingly parallel dataflow graph, 2 significant sparsity in model parameters and intermediate results, and 3 resilience to noisy computation and storage.
Exploiting these characteristics can offer significantly improved performance and energy efficiency. These chips contain CPUs, peripherals, on- chip memory and custom accelerators to allow us to tune and characterize the efficiency and resilience of deep learning algorithms in custom silicon.
International Solid-State Circuits Conference, Deep Learning. Compared to a standard search solutions, Bayesian optimization: Generates better samples: on average, 4. Discovers optimal designs faster, finding similar results with 3.Skip to Main Content.
Email Address. Sign In. Access provided by: anon Sign Out. Energy-Efficient Neural Network Accelerator Based on Outlier-Aware Low-Precision Computation Abstract: Owing to the presence of large values, which we call outliers, conventional methods of quantization fail to achieve significantly low precision, e.
In this study, we propose a hardware accelerator, called the outlier-aware accelerator OLAccel. It performs dense and low-precision computations for a majority of data weights and activations while efficiently handling a small number of sparse and high-precision outliers e. The OLAccel is based on 4-bit multiply-accumulate MAC units and handles outlier weights and activations in a different manner.A friendly introduction to Convolutional Neural Networks and Image Recognition
In order to avoid coherence problem due to updates from low- and high-precision computation units, both units update partial sums in a pipelined manner. Our experiments show that the OLAccel can reduce by The energy gain mostly comes from the memory components, the DRAM, and on-chip memory due to reduced precision.
Article :. DOI: Need Help?Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
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Access provided by: anon Sign Out. Most accelerators are designed for object detection and recognition algorithms that are performed on low-resolution images. However, real-time image super-resolution SR cannot be implemented on a typical accelerator because of the long execution cycles required to generate high-resolution HR images, such as those used in ultra-high-definition systems.
In this paper, we propose a novel CNN accelerator with efficient parallelization methods for SR applications. First, we propose a new methodology for optimizing the deconvolutional neural networks DCNNs used for increasing feature maps. Second, we propose a novel method to optimize CNN dataflow so that the SR algorithm can be driven at low power in display applications. Finally, we quantize and compress a DCNN-based SR algorithm into an optimal model for efficient inference using on-chip memory.
We present an energy-efficient architecture for SR and validate our architecture on a mobile panel with quad-high-definition resolution. Our experimental results show that, with the same hardware resources, the proposed DCNN accelerator achieves a throughput up to times greater than that of a conventional DCNN accelerator.
In addition, our SR system achieves an energy efficiency of Furthermore, we demonstrate that our system can restore HR images to a high quality while greatly reducing the data bit-width and the number of parameters compared with conventional SR algorithms.
Article :. Date of Publication: 20 December DOI: Need Help?Machine-learning techniques have recently been proved to be successful in various domains, especially in emerging commercial applications. As a set of machine-learning techniques, artificial neural networks ANNsrequiring considerable amount of computation and memory, are one of the most popular algorithms and have been applied in a broad range of applications such as speech recognition, face identification, natural language processing, ect.
Conventionally, as a straightforward way, conventional CPUs and GPUs are energy-inefficient due to their excessive effort for flexibility. According to the aforementioned situation, in recent years, many researchers have proposed a number of neural network accelerators to achieve high performance and low power consumption.
Thus, the main purpose of this literature is to briefly review recent related works, as well as the DianNao-family accelerators. In summary, this review can serve as a reference for hardware researchers in the area of neural networks. This is a preview of subscription content, log in to check access. A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics,5 4 : — Hebb D O.
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London: Psychology Press, Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review,65 6 : Werbos P. Beyond regression: new tools for prediction and analysis in the behavioral sciences.
Dissertation for the Doctoral Degree. Cambridge, MA: Harvard University, A fast learning algorithm for deep belief nets. Neural Computation,18 7 : — Bengio Y. Learning deep architectures for AI. Foundations and Trends in Machine Learning,2 1 : 1— Williams R J, Zipser D. A learning algorithm for continually running fully recurrent neural networks. Neural Computation,1 2 : — Back A, Tsoi A. Neural Computation,3 3 : — Local feedback multilayered networks. Neural Computation,4 1 : — Evolutionary artificial neural networks: a review.
Artificial Intelligence Review,39 3 : — Heart disease diagnosis utilizing hybrid fuzzy wavelet neural network and teaching learning based optimization algorithm. Advances in Artificial Neural Systems, Email: eyeriss at mit dot edu. Deep neural networks DNNs are currently widely used for many AI applications including computer vision, speech recognition, robotics, etc.
While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, designing efficient hardware architectures for deep neural networks is an important step towards enabling the wide deployment of DNNs in AI systems. This tutorial provides a brief recap on the basics of deep neural networks and is for those who are interested in understanding how those models are mapping to hardware architectures.
We will provide frameworks for understanding the design space for deep neural network accelerators including managing data movement, handling sparsity, and importance of flexibility. This is an intermediate-level tutorial that will go beyond the material in the previous incarnations of this tutorial.
Our book based on the tutorial "Efficient Processing of Deep Neural Networks" is available for pre-order at here. This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks DNNs. Readers will find a structured introduction to the field as well as a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.
An excerpt of the book on "Advanced Technologies" available at here. Understand the key design considerations for DNN Be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics Understand the tradeoffs between various architectures and platforms Assess the utility of various optimization approaches Understand recent implementation trends and opportunities.
Chen, T. Emer, V. Yang, A. Howard, B. Chen, X. Zhang, A. Go, V. Sze, H. Sze, T. Yang, Y. Chen, J.Skip to Main Content.
Several works have designed specific BNN accelerators and showed very promising results. Nevertheless, only part of the neural network is binarized in their architecture and the benefits of binary operations were not fully exploited. In this work, we propose the first fully binarized convolutional neural network accelerator FBNA architecture, in which all convolutional operations are binarized and unified, even including the first layer and padding.
The fully unified architecture provides more resource, parallelism and scalability optimization opportunities. Compared with the state-of-the-art BNN accelerator, our evaluation results show 3.
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DOI: Need Help?GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. A Chisel3 implementation of a fully connected neural network accelerator, DANA, supporting inference or learning.
DANA follows a transactional model of computation supporting simultaneous multithreading of transactions . This is currently compatibile with rocket-chip:fae9 -- an older rocket-chip version used by fpga-zynq. Clone this repo, add DANA, and build:.
This is not, at present, a standalone repository and must be cloned inside of an existing Rocket Chip clone.
The following will grab a supported version of rocket-chip and clone DANA inside of it:. Go ahead and build the version of the toolchain pointed at by the rocket-chip repository. This requires setting the RISCV environment variable and satisfying any dependencies required to build the toolchain. The Makefile just needs to know what configuration we're using and that we have additional Chisel code located in the dana directory:.
Convolutional Neural Network (CNN) Compact Accelerator IP
We provide bare-metal test programs inside the tests directory. For debugging or running the emulator more verbosely, you have the option of either relying on Chisel's printf or building a version of the emulator that supports full VCD dumping. Note: Rocket Chip dumps information every cycle and it is often useful to grep for the exact printf that you're looking for.
You can build a "debug" version of the emulator which provides full support for generating vcd traces with:. To further reduce the size of the VCD file we provide a tool that prunes a VCD file to only include signals in a specific module and it's children, vcd-prune.Sostitutiva
Example usage to only emit DANA signals:. There are a few remaining things that we're working on closing out which limit the set of available features.T7 plasmid
Currently, the neural network configuration must fit completely in one of DANA's configuration cache memories. DANA's neural network configuration format using bit internal pointers meaning that networks up to 4GiB are theoretically supported. While neural network configurations are loaded from the memory of the microprocessor, all input and output data is transferred from Rocket to DANA hardware through the Rocket Custom Coprocessor RoCC register interface.
We have plans to enable asynchronous transfer through in-memory queues. Additional documentation can be found in the doc directory or in some of our publications.
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