DOCSLIB.ORG

Fastwave: Accelerating Autoregressive Convolutional Neural Networks on FPGA

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Fastwave: Accelerating Autoregressive Convolutional Neural Networks on FPGA FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA Shehzeen Hussain∗, Mojan Javaheripi∗, Paarth Neekharay, Ryan Kastnery and Farinaz Koushanfar∗ ∗UC San Diego Department of Electrical and Computer Engineering yUC San Diego Department of Computer Science Email: [email protected], [email protected] Abstract—Autoregressive convolutional neural networks the inference still remains sequential and CNN based models (CNNs) have been widely exploited for sequence generation are usually very deep. However, to overcome this problem, tasks such as audio synthesis, language modeling and neural Fast-Wavenet [11] exploits the temporal dependencies by machine translation. WaveNet is a deep autoregressive CNN composed of several stacked layers of dilated convolution that caching redundant computations using fixed-length convolu- is used for sequence generation. While WaveNet produces tional queues and thus makes the generation time linear in state-of-the art audio generation results, the naive inference length of the sequence. Such efforts have made it feasible implementation is quite slow; it takes a few minutes to generate to use autoregressive CNNs for practical sequence generation just one second of audio on a high-end GPU. In this work, we applications, as an alternative to RNN-based models. develop the first accelerator platform FastWave for autoregressive convolutional neural networks, and address the associated design While the Fast-Wavenet algorithm provides a speed-up in challenges. We design the Fast-Wavenet inference model in audio generation over na¨ıve Wavenet implementation, the Vivado HLS and perform a wide range of optimizations generation time is still high, even on a high-end GPU. It including fixed-point implementation, array partitioning and takes 120 seconds to generate 1 second of audio using Fast- pipelining. Our model uses a fully parameterized parallel Wavenet implementation on a NVIDIA TITAN Xp GPU. Prior architecture for fast matrix-vector multiplication that enables per-layer customized latency fine-tuning for further throughput works have shown FPGAs to be successful in accelerating the improvement. Our experiments comparatively assess the trade- inference of pre-trained neural networks by providing custom off between throughput and resource utilization for various data paths to achieve high parallelism. A vast amount of optimizations. Our best WaveNet design on the Xilinx XCVU13P such research focuses on accelerating neural networks in the FPGA that uses only on-chip memory, achieves 66× faster image domain [12], [13], speech recognition [14], [15] and generation speed compared to CPU implementation and 11× faster generation speed than GPU implementation. language modelling [16]. To the best of our knowledge, similar efforts have not been made for accelerating neural networks I. INTRODUCTION for speech/audio synthesis. Autoregressive convolutional models achieve state-of-the- We aim to accelerate audio synthesis using the autoregres- art results in audio [1]–[4] and language domains [5], [6] with sive CNN model - WaveNet on FPGA. The primary challenges respect to both estimating the data distribution and generating in deploying auto-regressive CNN inference on FPGA are high-quality samples. Wavenet [1] is an example of autoregres- designing modules for dilated convolutional layers, buffers for sive convolutional network, used for modelling audio for appli- storing redundant computations using convolutional queues, cations such as text-to-speech (TTS) synthesis and music gen- and dealing with the large depth of these networks which eration. WaveNet has been rated by human listeners to provide is necessary to maintain high audio quality. In this work, substantially more natural sounding audio when compared to we address these challenges of deploying large autoregressive the best existing parametric and concatenative systems in TTS convolutional models on FPGAs and perform a wide range applications for both English and Mandarin [1]. Popular cloud of application and architectural optimizations. Furthermore, based TTS synthesis systems such as Google Now and Google we comprehensively analyze and compare the performance Assistant, that produce natural sounding speech, are built on of Fast-Wavenet implementation on FPGA with the CPU and WaveNet architecture [4], [7]. Alongside achieving state-of- GPU counterparts. the art results in the audio domain, convolutional models Summary of Contributions: are prominent for natural language modeling tasks like text • Creation of the first accelerator platform for autoregres- generation and machine translation [5]. sive convolutional neural networks. We deploy the fast Generally, both autoregressive convolutional neural net- inference model Fast-Wavenet [11] on Xilinx XCVU13P works (CNNs) and Recurrent Neural Networks (RNNs) [8] FPGA which achieves 11 times faster generation speed are widely popular for sequence modelling tasks. The main than a high-end GPU and 66 times faster generation speed advantage of CNN based models is that they can achieve than a high-end CPU. higher parallelism during training and can capture longer time- • Development of reconfigurable basic blocks pertinent to dependencies as compared to RNN based models [9], [10]. autoregressive convolutional networks i.e., dilated causal However, this comes at a cost of slower inference, since convolutional layers, convolutional queues, and fully con- nected layer. Our operations are powered by a fully- learnable parameters of Wavenet model. During inference, customizable matrix-multiplication engine that uses two next-sample audio (xt) generation is performed by sampling levels of parallelism controlled by tunable parameters. from the conditional probability distribution given all of the • Creation of an end-to-end framework that accesses only previous samples, P (xtjxt−1; :::; x1; x0; λ). on-chip memory thereby ensuring high throughput and One possible method for modeling the probability den- avoiding any latency caused by communication with off- sity is via a stack of causal convolutional layers as de- chip memory. picted in Figure 1(a). The input passes through this stack • Exploration of the design space using different architec- of convolutional layers and gated activation functions and tural and application optimizations, as well as comparing finally through a softmax layer to get the posterior proba- the performance and resource usage. We present extensive bility P (xtjxt−1; :::; x1; x0). The downside of this approach evaluation of throughput and power efficiency for our is that in order to model long temporal dependencies from fully optimized and baseline designs. samples far in the past, the causal convolutional network II. BACKGROUND requires either many layers or large filters to increase the receptive field. In general, the receptive field is calculated as In this section, we provide a background on autoregressive # of layers + filter + 1 which gives a receptive field convolutional neural networks. We choose WaveNet as an size of 5 in the architecture shown in Figure 1(a). To address this ideal representation of such models, and describe its overall problem, WaveNet leverages dilated convolutions [20], [21] generative architecture. We first elaborate on the 1D convo- which deliver higher receptive fields without significant in- lution operation as it is the core computation performed in crease in computational cost of the model. Dilated convolution the WaveNet model. Next, we explain WaveNet and its more is equivalent to performing convolutions with dilated filters efficient inference algorithm called Fast-Wavenet. where the size of the filter is expanded by filling the empty A. 1D Convolution positions with zeros. In practice, this is achieved by performing The 1D convolution operation is performed by sliding a one a convolution where the filter skips input values with a certain dimensional kernel over a 1D input signal. Each output value step. at position i is produced by calculating the dot product of the Fig 1(b) illustrates a network with dilated causal convolu- kernel and the overlapping values of the input signal, starting tions for dilation values of 1, 2, 4, and 8. Here, the input from position i. More formally, for an input vector a of length nodes are shown with color blue and the output is shown n and a kernel k of length m, the 1D convolution is calculated with orange. Each edge in the graph correspond to a 1- as follows: dimensional convolution (See section II-A), more generally m (a ∗ k) = σ k × a m (1) i j=1 j i−j+ 2 a matrix multiplication. Due to the binary tree structure of the network, the time complexity of computing output at where i is an arbitrary index in the output vector, which has each time-step is O(2L) where L is the number of layers a total length of n − m + 1. The subscripts denote the indices in the network, which gets highly undesirable as L increases. of the kernel/input vectors. Similarly, the total memory required to store the inputs, output, B. WaveNet and Autoregressive CNNs and the intermediate layer features is O(2L). Autoregressive Neural Networks are popularly used for sequence generation tasks which rely on ancestral sampling i.e. the predictive distribution for each sample in the sequence is conditioned on all previous ones. While RNNs are popular autoregressive models, they do not exhibit high receptive field making them unsuitable for modeling sequences with long- term dependencies like audio [9]. In contrast, autoregressive CNN based models
Load more

Recommended publications
  • Face Recognition: a Convolutional Neural-Network Approach
    98 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 1, JANUARY 1997 Face Recognition: A Convolutional Neural-Network Approach Steve Lawrence, Member, IEEE, C. Lee Giles, Senior Member, IEEE, Ah Chung Tsoi, Senior Member, IEEE, and Andrew D. Back, Member, IEEE Abstract— Faces represent complex multidimensional mean- include fingerprints [4], speech [7], signature dynamics [36], ingful visual stimuli and developing a computational model for and face recognition [8]. Sales of identity verification products face recognition is difficult. We present a hybrid neural-network exceed $100 million [29]. Face recognition has the benefit of solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map being a passive, nonintrusive system for verifying personal (SOM) neural network, and a convolutional neural network. identity. The techniques used in the best face recognition The SOM provides a quantization of the image samples into a systems may depend on the application of the system. We topological space where inputs that are nearby in the original can identify at least two broad categories of face recognition space are also nearby in the output space, thereby providing systems. dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides for 1) We want to find a person within a large database of partial invariance to translation, rotation, scale, and deformation. faces (e.g., in a police database). These systems typically The convolutional network extracts successively larger features return a list of the most likely people in the database in a hierarchical set of layers. We present results using the [34].
    [Show full text]
  • CNN Architectures
    Lecture 9: CNN Architectures Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 1 May 2, 2017 Administrative A2 due Thu May 4 Midterm: In-class Tue May 9. Covers material through Thu May 4 lecture. Poster session: Tue June 6, 12-3pm Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 2 May 2, 2017 Last time: Deep learning frameworks Paddle (Baidu) Caffe Caffe2 (UC Berkeley) (Facebook) CNTK (Microsoft) Torch PyTorch (NYU / Facebook) (Facebook) MXNet (Amazon) Developed by U Washington, CMU, MIT, Hong Kong U, etc but main framework of Theano TensorFlow choice at AWS (U Montreal) (Google) And others... Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 3 May 2, 2017 Last time: Deep learning frameworks (1) Easily build big computational graphs (2) Easily compute gradients in computational graphs (3) Run it all efficiently on GPU (wrap cuDNN, cuBLAS, etc) Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 4 May 2, 2017 Last time: Deep learning frameworks Modularized layers that define forward and backward pass Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 5 May 2, 2017 Last time: Deep learning frameworks Define model architecture as a sequence of layers Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 6 May 2, 2017 Today: CNN Architectures Case Studies - AlexNet - VGG - GoogLeNet - ResNet Also.... - NiN (Network in Network) - DenseNet - Wide ResNet - FractalNet - ResNeXT - SqueezeNet - Stochastic Depth Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 7 May 2, 2017 Review: LeNet-5 [LeCun et al., 1998] Conv filters were 5x5, applied at stride 1 Subsampling (Pooling) layers were 2x2 applied at stride 2 i.e.
    [Show full text]
  • Deep Learning Architectures for Sequence Processing
    Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright © 2021. All rights reserved. Draft of September 21, 2021. CHAPTER Deep Learning Architectures 9 for Sequence Processing Time will explain. Jane Austen, Persuasion Language is an inherently temporal phenomenon. Spoken language is a sequence of acoustic events over time, and we comprehend and produce both spoken and written language as a continuous input stream. The temporal nature of language is reflected in the metaphors we use; we talk of the flow of conversations, news feeds, and twitter streams, all of which emphasize that language is a sequence that unfolds in time. This temporal nature is reflected in some of the algorithms we use to process lan- guage. For example, the Viterbi algorithm applied to HMM part-of-speech tagging, proceeds through the input a word at a time, carrying forward information gleaned along the way. Yet other machine learning approaches, like those we’ve studied for sentiment analysis or other text classification tasks don’t have this temporal nature – they assume simultaneous access to all aspects of their input. The feedforward networks of Chapter 7 also assumed simultaneous access, al- though they also had a simple model for time. Recall that we applied feedforward networks to language modeling by having them look only at a fixed-size window of words, and then sliding this window over the input, making independent predictions along the way. Fig. 9.1, reproduced from Chapter 7, shows a neural language model with window size 3 predicting what word follows the input for all the. Subsequent words are predicted by sliding the window forward a word at a time.
    [Show full text]
  • Introduction-To-Deep-Learning.Pdf
    Introduction to Deep Learning Demystifying Neural Networks Agenda Introduction to deep learning: • What is deep learning? • Speaking deep learning: network types, development frameworks and network models • Deep learning development flow • Application spaces Deep learning introduction ARTIFICIAL INTELLIGENCE Broad area which enables computers to mimic human behavior MACHINE LEARNING Usage of statistical tools enables machines to learn from experience (data) – need to be told DEEP LEARNING Learn from its own method of computing - its own brain Why is deep learning useful? Good at classification, clustering and predictive analysis What is deep learning? Deep learning is way of classifying, clustering, and predicting things by using a neural network that has been trained on vast amounts of data. Picture of deep learning demo done by TI’s vehicles road signs person background automotive driver assistance systems (ADAS) team. What is deep learning? Deep learning is way of classifying, clustering, and predicting things by using a neural network that has been trained on vast amounts of data. Machine Music Time of Flight Data Pictures/Video …any type of data Speech you want to classify, Radar cluster or predict What is deep learning? • Deep learning has its roots in neural networks. • Neural networks are sets of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. Biologocal Artificial Biological neuron neuron neuron dendrites inputs synapses weight axon output axon summation and cell body threshold dendrites synapses cell body Node = neuron Inputs Weights Summation W1 x1 & threshold W x 2 2 Output Inputs W3 Output x3 y layer = stack of ∑ ƒ(x) neurons Wn xn ∑=sum(w*x) Artificial neuron 6 What is deep learning? Deep learning creates many layers of neurons, attempting to learn structured representation of big data, layer by layer.
    [Show full text]
  • Face Recognition Using Popular Deep Net Architectures: a Brief Comparative Study
    future internet Article Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study Tony Gwyn 1,* , Kaushik Roy 1 and Mustafa Atay 2 1 Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA; [email protected] 2 Department of Computer Science, Winston-Salem State University, Winston-Salem, NC 27110, USA; [email protected] * Correspondence: [email protected] Abstract: In the realm of computer security, the username/password standard is becoming increas- ingly antiquated. Usage of the same username and password across various accounts can leave a user open to potential vulnerabilities. Authentication methods of the future need to maintain the ability to provide secure access without a reduction in speed. Facial recognition technologies are quickly becoming integral parts of user security, allowing for a secondary level of user authentication. Augmenting traditional username and password security with facial biometrics has already seen impressive results; however, studying these techniques is necessary to determine how effective these methods are within various parameters. A Convolutional Neural Network (CNN) is a powerful classification approach which is often used for image identification and verification. Quite recently, CNNs have shown great promise in the area of facial image recognition. The comparative study proposed in this paper offers an in-depth analysis of several state-of-the-art deep learning based- facial recognition technologies, to determine via accuracy and other metrics which of those are most effective. In our study, VGG-16 and VGG-19 showed the highest levels of image recognition accuracy, Citation: Gwyn, T.; Roy, K.; Atay, M. as well as F1-Score.
    [Show full text]
  • Recurrent Neural Network
    Recurrent Neural Network TINGWU WANG, MACHINE LEARNING GROUP, UNIVERSITY OF TORONTO FOR CSC 2541, SPORT ANALYTICS Contents 1. Why do we need Recurrent Neural Network? 1. What Problems are Normal CNNs good at? 2. What are Sequence Tasks? 3. Ways to Deal with Sequence Labeling. 2. Math in a Vanilla Recurrent Neural Network 1. Vanilla Forward Pass 2. Vanilla Backward Pass 3. Vanilla Bidirectional Pass 4. Training of Vanilla RNN 5. Vanishing and exploding gradient problems 3. From Vanilla to LSTM 1. Definition 2. Forward Pass 3. Backward Pass 4. Miscellaneous 1. More than Language Model 2. GRU 5. Implementing RNN in Tensorflow Part One Why do we need Recurrent Neural Network? 1. What Problems are Normal CNNs good at? 2. What is Sequence Learning? 3. Ways to Deal with Sequence Labeling. 1. What Problems are CNNs normally good at? 1. Image classification as a naive example 1. Input: one image. 2. Output: the probability distribution of classes. 3. You need to provide one guess (output), and to do that you only need to look at one image (input). P(Cat|image) = 0.1 P(Panda|image) = 0.9 2. What is Sequence Learning? 1. Sequence learning is the study of machine learning algorithms designed for sequential data [1]. 2. Language model is one of the most interesting topics that use sequence labeling. 1. Language Translation 1. Understand the meaning of each word, and the relationship between words 2. Input: one sentence in German input = "Ich will stark Steuern senken" 3. Output: one sentence in English output = "I want to cut taxes bigly" (big league?) 2.
    [Show full text]
  • Neural Networks and Backpropagation
    10-601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University Neural Networks and Backpropagation Neural Net Readings: Murphy -- Matt Gormley Bishop 5 Lecture 20 HTF 11 Mitchell 4 April 3, 2017 1 Reminders • Homework 6: Unsupervised Learning – Release: Wed, Mar. 22 – Due: Mon, Apr. 03 at 11:59pm • Homework 5 (Part II): Peer Review – Expectation: You Release: Wed, Mar. 29 should spend at most 1 – Due: Wed, Apr. 05 at 11:59pm hour on your reviews • Peer Tutoring 2 Neural Networks Outline • Logistic Regression (Recap) – Data, Model, Learning, Prediction • Neural Networks – A Recipe for Machine Learning Last Lecture – Visual Notation for Neural Networks – Example: Logistic Regression Output Surface – 2-Layer Neural Network – 3-Layer Neural Network • Neural Net Architectures – Objective Functions – Activation Functions • Backpropagation – Basic Chain Rule (of calculus) This Lecture – Chain Rule for Arbitrary Computation Graph – Backpropagation Algorithm – Module-based Automatic Differentiation (Autodiff) 3 DECISION BOUNDARY EXAMPLES 4 Example #1: Diagonal Band 5 Example #2: One Pocket 6 Example #3: Four Gaussians 7 Example #4: Two Pockets 8 Example #1: Diagonal Band 9 Example #1: Diagonal Band 10 Example #1: Diagonal Band Error in slides: “layers” should read “number of hidden units” All the neural networks in this section used 1 hidden layer. 11 Example #1: Diagonal Band 12 Example #1: Diagonal Band 13 Example #1: Diagonal Band 14 Example #1: Diagonal Band 15 Example #2: One Pocket
    [Show full text]
  • Deep Layer Aggregation
    Deep Layer Aggregation Fisher Yu Dequan Wang Evan Shelhamer Trevor Darrell UC Berkeley Abstract Visual recognition requires rich representations that span levels from low to high, scales from small to large, and resolutions from fine to coarse. Even with the depth of fea- Dense Connections Feature Pyramids tures in a convolutional network, a layer in isolation is not enough: compounding and aggregating these representa- tions improves inference of what and where. Architectural + efforts are exploring many dimensions for network back- bones, designing deeper or wider architectures, but how to best aggregate layers and blocks across a network deserves Deep Layer Aggregation further attention. Although skip connections have been in- Figure 1: Deep layer aggregation unifies semantic and spa- corporated to combine layers, these connections have been tial fusion to better capture what and where. Our aggregation “shallow” themselves, and only fuse by simple, one-step op- architectures encompass and extend densely connected net- erations. We augment standard architectures with deeper works and feature pyramid networks with hierarchical and aggregation to better fuse information across layers. Our iterative skip connections that deepen the representation and deep layer aggregation structures iteratively and hierarchi- refine resolution. cally merge the feature hierarchy to make networks with better accuracy and fewer parameters. Experiments across architectures and tasks show that deep layer aggregation improves recognition and resolution compared to existing riers, different blocks or modules have been incorporated branching and merging schemes. to balance and temper these quantities, such as bottlenecks for dimensionality reduction [29, 39, 17] or residual, gated, and concatenative connections for feature and gradient prop- agation [17, 38, 19].
    [Show full text]
  • Chapter 10: Artificial Neural Networks
    Chapter 10: Artificial Neural Networks Dr. Xudong Liu Assistant Professor School of Computing University of North Florida Monday, 9/30/2019 1 / 17 Overview 1 Artificial neuron: linear threshold unit (LTU) 2 Perceptron 3 Multi-Layer Perceptron (MLP), Deep Neural Networks (DNN) 4 Learning ANN's: Error Backpropagation Overview 2 / 17 Differential Calculus In this course, I shall stick to Leibniz's notation. The derivative of a function at an input point, when it exists, is the slope of the tangent line to the graph of the function. 2 0 dy Let y = f (x) = x + 2x + 1. Then, f (x) = dx = 2x + 2. A partial derivative of a function of several variables is its derivative with respect to one of those variables, with the others held constant. 2 2 @z Let z = f (x; y) = x + xy + y . Then, @x = 2x + y. The chain rule is a formula for computing the derivative of the composition of two or more functions. dz dz dy 0 0 Let z = f (y) and y = g(x). Then, dx = dy · dx = f (y) · g (x). 1 Nice thing about the sigmoid function f (x) = 1+e−x : f 0(x) = f (x) · (1 − f (x)). Some Preliminaries 3 / 17 Artificial Neuron Artificial neuron, also called linear threshold unit (LTU), by McCulloch and Pitts, 1943: with one or more numeric inputs, it produces a weighted sum of them, applies an activation function, and outputs the result. Common activation functions: step function and sigmoid function. Artificial Neuron 4 / 17 Linear Threshold Unit (LTU) Below is an LTU with the activation function being the step function.
    [Show full text]
  • Increasing Neurons Or Deepening Layers in Forecasting Maximum Temperature Time Series?
    atmosphere Article Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series? Trang Thi Kieu Tran 1, Taesam Lee 1,* and Jong-Suk Kim 2,* 1 Department of Civil Engineering, ERI, Gyeongsang National University, 501 Jinju-daero, Jinju 660-701, Korea; [email protected] 2 State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China * Correspondence: [email protected] (T.L.); [email protected] (J.-S.K.) Received: 8 September 2020; Accepted: 7 October 2020; Published: 9 October 2020 Abstract: Weather forecasting, especially that of extreme climatic events, has gained considerable attention among researchers due to their impacts on natural ecosystems and human life. The applicability of artificial neural networks (ANNs) in non-linear process forecasting has significantly contributed to hydro-climatology. The efficiency of neural network functions depends on the network structure and parameters. This study proposed a new approach to forecasting a one-day-ahead maximum temperature time series for South Korea to discuss the relationship between network specifications and performance by employing various scenarios for the number of parameters and hidden layers in the ANN model. Specifically, a different number of trainable parameters (i.e., the total number of weights and bias) and distinctive numbers of hidden layers were compared for system-performance effects. If the parameter sizes were too large, the root mean square error (RMSE) would be generally increased, and the model’s ability was impaired. Besides, too many hidden layers would reduce the system prediction if the number of parameters was high. The number of parameters and hidden layers affected the performance of ANN models for time series forecasting competitively.
    [Show full text]
  • Introduction to Machine Learning CMU-10701 Deep Learning
    Introduction to Machine Learning CMU-10701 Deep Learning Barnabás Póczos & Aarti Singh Credits Many of the pictures, results, and other materials are taken from: Ruslan Salakhutdinov Joshua Bengio Geoffrey Hinton Yann LeCun 2 Contents Definition and Motivation History of Deep architectures Deep architectures Convolutional networks Deep Belief networks Applications 3 Deep architectures Defintion: Deep architectures are composed of multiple levels of non-linear operations, such as neural nets with many hidden layers. Output layer Hidden layers Input layer 4 Goal of Deep architectures Goal: Deep learning methods aim at . learning feature hierarchies . where features from higher levels of the hierarchy are formed by lower level features. edges, local shapes, object parts Low level representation 5 Figure is from Yoshua Bengio Neurobiological Motivation Most current learning algorithms are shallow architectures (1-3 levels) (SVM, kNN, MoG, KDE, Parzen Kernel regression, PCA, Perceptron,…) The mammal brain is organized in a deep architecture (Serre, Kreiman, Kouh, Cadieu, Knoblich, & Poggio, 2007) (E.g. visual system has 5 to 10 levels) 6 Deep Learning History Inspired by the architectural depth of the brain, researchers wanted for decades to train deep multi-layer neural networks. No successful attempts were reported before 2006 … Researchers reported positive experimental results with typically two or three levels (i.e. one or two hidden layers), but training deeper networks consistently yielded poorer results. Exception: convolutional neural networks, LeCun 1998 SVM: Vapnik and his co-workers developed the Support Vector Machine (1993). It is a shallow architecture. Digression: In the 1990’s, many researchers abandoned neural networks with multiple adaptive hidden layers because SVMs worked better, and there was no successful attempts to train deep networks.
    [Show full text]
  • Introduction to Machine Learning Deep Learning
    Introduction to Machine Learning Deep Learning Barnabás Póczos Credits Many of the pictures, results, and other materials are taken from: Ruslan Salakhutdinov Joshua Bengio Geoffrey Hinton Yann LeCun 2 Contents Definition and Motivation Deep architectures Convolutional networks Applications 3 Deep architectures Defintion: Deep architectures are composed of multiple levels of non-linear operations, such as neural nets with many hidden layers. Output layer Hidden layers Input layer 4 Goal of Deep architectures Goal: Deep learning methods aim at ▪ learning feature hierarchies ▪ where features from higher levels of the hierarchy are formed by lower level features. edges, local shapes, object parts Low level representation 5 Figure is from Yoshua Bengio Theoretical Advantages of Deep Architectures Some complicated functions cannot be efficiently represented (in terms of number of tunable elements) by architectures that are too shallow. Deep architectures might be able to represent some functions otherwise not efficiently representable. More formally: Functions that can be compactly represented by a depth k architecture might require an exponential number of computational elements to be represented by a depth k − 1 architecture The consequences are ▪ Computational: We don’t need exponentially many elements in the layers ▪ Statistical: poor generalization may be expected when using an insufficiently deep architecture for representing some functions. 9 Theoretical Advantages of Deep Architectures The Polynomial circuit: 10 Deep Convolutional Networks 11 Deep Convolutional Networks Compared to standard feedforward neural networks with similarly-sized layers, ▪ CNNs have much fewer connections and parameters ▪ and so they are easier to train, ▪ while their theoretically-best performance is likely to be only slightly worse.
    [Show full text]