High utilization requires high bandwidth memory and clever memory management to keep the compute busy on the chip. In data parallelism, the mini-batch is split among the worker nodes with each node having the full model and processing a piece of the mini-batch, known as the node-batch. Jiong graduated from Shanghai Jiao Tong University as a master in computer science. For “ConvNet” topologies, dummy dataset was used. He is an internationally recognized expert on big data, cloud and distributed machine learning; he is the program co-chair of Strata Data Conference Beijing, a committer and PMC member of Apache Spark project, and the creator of BigDL project, a distributed deep learning framework on Apache Spark. Deep Learning Frameworks: Intel Caffe, internal version. The US will continue to hold the maximum market share for the next few years as it the home of many world’s top deep learning companies. 6 Layer deep Dense ANN for MNIST image Classification. These include label and prepare data, choose an algorithm and train the model. Topology specs from https://github.com/dmlc/mxnet/tree/master/example/image-classification/symbols. Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. Even in the biggest cities, we can help save time for eye-care professionals and enable them to devote themselves to patients with the most serious problems. TensorFlow: (https://github.com/tensorflow/tensorflow), commit id 207203253b6f8ea5e938a512798429f91d5b4e7e. Training the model involves adjusting the model parameters to reduce this loss. In addition, it allows the reuse of pre-trained models from Caffe, Torch*, and TensorFlow. Clemson University researchers applied 1.1M AWS EC2 vCPUs on Intel processors to study topic modeling, a component of natural language processing. In gradient descent (GD), also known as steepest descent, the loss function for a particular model defined by the set of weights is computed over the entire dataset. The weights are updated by moving in the direction opposite to the gradient; that is, moving towards the local minimum: updated-weights = current-weights – learning-rate * gradient. Build the data plumbing that allows for data scientists to process huge amounts of data. In July 2017, Intel launched the Intel Xeon Scalable processor family built on 14 nm process technology. Furthermore, it does not match single node performance and therefore it is more difficult to debug. Jiong Gong is a senior software engineer with Intel’s Software and Service Group where he is responsible for the architectural design of Intel Optimized Caffe, making optimizations to show its performance advantage on both single-node and multi-node IA platforms. Through early detection, we provide opportunities for earlier diagnosis and treatment, to help preserve vision.”. This becomes more problematic when distributing the computational workload of the small mini-batch across several worker nodes. In this 4-part article, we explore each of the main three factors outlined contributing to record-setting speed, and provide various examples of commercial use cases using Intel Xeon processors for deep learning training. The behavior of SGD approaches the behavior of GD as the mini-batch sizes increase and become the same when the mini-batch size equals the entire dataset. Image courtesy of Amir Gholami, Peter Jin, Yang You, Kurt Keutzer, and the PALLAS group at UC Berkeley. Deep learning primitives are optimized in the Intel MKL-DNN library by incorporating prefetching, data layout, cache-blocking, data reuse, vectorization, and register-blocking strategies. I initially want to get a 9900k because I want to use the PC for gaming too, but 9900k has only 16 PCIe lanes, so it may not suitable. During the execution step the data is fed to the network in a plain layout like BCWH (batch, channel, width, height) and is converted to a SIMD-friendly layout. Furthermore, based on the client’s history and background, the deep learning systems can offer contextual and evidence-based reasoning by engaging in dialogue with humans. GE Healthcare applied Intel Xeon E5-2650v4 processors for GE's deep learning CT imaging classification inference workloads. MKL-DNN primitives are shown in blue. Deep learning is largely used in image recognition, NLP processing and speech recognition software. Inference measured with --caffe time -forward_only -engine MKL2017option, training measured with –forward_backward_only option. Gigaspace built a system to automatically route user requests to appropriate specialists in call centers by using a natural language processing (NLP) model trained in BigDL on Intel Xeon processors. SSD: Intel SSD DC S3500 Series (480GB, 2.5in SATA 6Gb/s, 20nm, MLC). They studied how human language is processed by computers with nearly half a million topic modeling experiments. AI is projected to create 2.3 million related jobs by 2020, according to Gartner. These include feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). These results were obtained on Intel® Xeon® Scalable processors (formerly codename Skylake-SP). For “ConvNet” topologies, dummy dataset was used. Ring is optimal for bandwidth; for large data communication it scales at O(1) with the number of nodes. This is known as synchronous SGD (SSGD). There are other variants that speed up the training process by accumulating velocity (known as momentum) in the direction of the opposite of gradients, or that reduce the data scientist’s burden of choosing a good learning rate by automatically modifying the learning rate depending on the norm of the gradients. News Feed and Sigma services train on CPUs, and Facer and Search algorithms train on both CPUs and GPUs. Intel MKL is a library that contains many mathematical functions and only some of them are used for deep learning. Deep learning is a class of machine learning algorithms that (pp199–200) uses multiple layers to progressively extract higher-level features from the raw input. First, using SMB-SGD is computational inexpensive and therefore each iteration is fast. An experienced leader in his field, he has led numerous teams accelerating the cloud, data center, artificial intelligence, mobile and Internet-of-things. For “ConvNet” topologies, dummy dataset was used. In addition, BigDL integrates Intel MKL and parallel computing techniques to achieve very high performance on Intel Xeon processor servers. Software optimization is essential to high compute utilization and improved performance. Frank graduated from University of Texas at Dallas with master degree in Electrical Engineering. The performance of these and other frameworks is expected to improve with further optimizations. In model parallelism, the mode is split among the worker nodes with each node working on the same mini-batch. CNTK also implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. OS drive: Seagate* Enterprise ST2000NX0253 2 TB 2.5" Internal Hard Drive. Intel C++ compiler 2017.4.196, Intel MKL small libraries version 2018.0.1.20171007. Frank Zhang is the Intel Optimized Caffe product manager from Intel Software and Service Group where he is responsible for product management of Intel Optimized Caffe deep learning framework development, product release and customer support. Deep Learning Systems. GCC 4.8.5, MKLML version 2017.0.2.20170110. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Details. Deep learning is a complicated process that’s fairly simple to explain. A subset of machine learning, which is itself a subset of artificial intelligence, DL is one way of implementing machine learning (automated data analysis) via what are called artificial neural networks — algorithms that effectively mimic the human brain’s structure and function. Inference measured with --caffe time -forward_only -engine MKL2017option, training measured with --forward_backward_only option. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). Deep learning is a subcategory of machine learning methods powered by artificial intelligence technologies. Machine Learning vs Deep Learning. CNTK allows the user to easily realize and combine popular model types. Platform: 2S Intel Xeon Platinum 8180 CPU @ 2.50GHz (28 cores), HT enabled, turbo enable; BIOS: SE5C620.86B.00.01.0004.071220170215; Memory: 376.28GB, 12slots / 32 GB / 2666 MHz; Disks: sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB; OS: CentOS Linux-7.3.1611-Core; Kernel: 3.10.0-327.22.2.el7.x86_64. Data with lower numerical precision will be added at a later time; lower precision can improve performance. Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact‘, OMP_NUM_THREADS=40. 2. While related in nature, subtle differences separate these fields of computer science. The main factors for these performance speeds are: This level of performance demonstrates that Intel Xeon processors are an excellent hardware platform for deep learning training. GPU (Graphics Processing Unit) : A programmable logic chip (processor) specialized for display functions. After 8K, the learning rate should increase proportional to the square root of the increased in mini-batch sizes. Inference measured with --caffe time -forward_only -engine MKL2017option, training measured with --forward_backward_only option. For other topologies, data was stored on local storage and cached in memory before training BVLC Caffe (http://github.com/BVLC/caffe), revision 91b09280f5233cafc62954c98ce8bc4c204e7475 (commit date 5/14/2017). The global machine learning (ML) market is estimated, to grow from $1.4 billion in 2017 to $8.8 billion by 2022, 5 Deep Learning Companies To Keep An Eye On In 2020. ’ GPU-accelerated deep learning frameworks offer flexibility to design and train custom deep neural networks and provide interfaces to commonly-used programming languages such as Python and C/C++, making NVIDIA one of the world’s leading deep learning developers. For example, inference across all available CPU cores on AlexNet*, GoogleNet* v1, ResNet-50*, and GoogleNet v3 with Apache MXNet on the Intel Xeon processor E5-2666 v3 (c4.8xlarge AWS* EC2* instance) can provide 111x, 109x, 66x, and 92x, respectively, higher throughput. In order to have a more targeted deep learning library and to collaborate with deep learning developers, Intel MKL-DNN was released open-source under an Apache 2 license with all the key building blocks necessary to build complex models. Kenneth strongly believes that blockchain will have as much impact as the Internet and e-commerce combined. They trained an agent to play a wide range of Atari 2600 games on 64 12-core Intel CPUs sometimes with perfect linear scaling (note: the article does not specify the specific Intel processors or interconnects used) in as little as 20 minutes per game. 300 original solar panel images augmented with 36-degree rotation were used in training. Over the past two years, Intel has diligently optimized deep learning functions achieving high utilization and enabling deep learning scientists to use their existing general-purpose Intel processors for deep learning training. Inference measured with “caffe time --forward_only” command, training measured with “caffe time” command. Inference measured with “caffe time --forward_only” command, training measured with “caffe time” command. The AVX-512 frequencies for multiple SKUs can be found at, When executing in 512-bit register port scheme on processors with two FMA unit, Port 0 FMA has a latency of 4 cycles, and Port 5 FMA has a latency of 6 cycles. The GoogleNet model was modified for this task. Popular deep learning frameworks are now incorporating these optimizations, increasing the effective performance delivered by a single server by over 100x in some cases. Distributing the computational requirement among multiple server nodes can reduce the time to train. Fatal Frame: An Underrated Horror Video Game Series. j. GCC 4.8.5, Intel MKL small libraries version 2018.0.20170425, interop parallelism threads set to 1 for alexnet, vgg benchmarks, 2 for googlenet benchmarks, intra op parallelism threads set to 40, data format used is NCHW, KMP_BLOCKTIME set to 1 for googlenet and vgg benchmarks, 30 for the alexnet benchmark. Topic models can used to discover the themes present across a collection of documents. For a variety of experiments mentioned below, I will present the time and utilization boosts that I observed. Qualcomm Incorporated is one of the world’s leading telecom companies, headquartered in San Diego. This is known as reduce/broadcast or just allreduce scheme (a list of allreduce options is discussed below). Deep Learning HDL Toolbox; Deep Learning Processor Customization and IP Generation; Custom Deep Learning Processor Generation to Meet Performance Requirements; On this page; Load Pretrained Series Network; Create Custom Processor Configuration; Create Workflow Object; Estimate LogoNet Performance; Create Modified Custom Processor Configuration During setup, the framework manages layout conversions from the framework to MKL-DNN and allocate temporary arrays if the appropriate output and input data layouts do not match. For deep learning training with several neural network layers or on massive sets of certain data, like 2D images, a GPU or other accelerators are ideal. Latest version neon results: https://www.intelnervana.com/neon-v2-3-0-significant-performance-boost-for-deep-speech-2-and-vgg-models/. The false negative rate consistently met the expected human-level accuracy. STREAM: 1-Node, 2 x Intel Xeon Platinum 8180 processor on Neon City with 384 GB Total Memory on Red Hat Enterprise Linux* 7.2-kernel 3.10.0-327 using STREAM AVX 512 Binaries. d. Platform: 2S Intel Xeon Platinum 8180 CPU @ 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. These gradients are then aggregated using some reduce algorithm to compute the gradient with respect to the overall mini-batch. Moreover, Sensory’s TrulySecure uses a deep learning approach in our face and voice recognition algorithms. Another things is new 10th Gen Intel Core i7-10750H processor with up to 5.0 GHz3 have a 6 cores. For “ConvNet” topologies, dummy dataset was used. Principal Engineer with Intel’s Artificial Intelligence Products Group (AIPG) where he designs AI solutions for Intel’s customers and provides technical leadership across Intel for AI products. // No product or component can be absolutely secure. Intel MKL-DNN allows industry and academic deep learning developers to distribute the library and contribute new or improved functions. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC). Syntiant Corp., a deep learning chip technology company advancing AI pervasiveness in edge devices, today announced the availability of its Syntiant® NDP120™ Neural Decision Processor™ (NDP), the latest generation of special purpose chips for audio and sensor processing for always-on applications in battery-powered devices. Intel MKL-DNN primitives are implemented in C with C and C++ API bindings for most widely used deep learning functions: There are multiple deep learning frameworks such as Caffe, TensorFlow, MXNet, PyTorch, etc. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Specifically, Dell EMC … Due to the benefits of running BigDL on Xeon-based Hadoop*/Spark clusters, such as simplicity, scalability, low TCO, BigDL has been widely adopted in the industry and developer community. Figure 3:  Operation graph flow. Additional parallelism across cores is important for high CPU utilization, such as parallelizing across a mini-batch using OpenMP*. Third, it is more likely to find a flat minimum since SMB-SGD better explores the solution space instead of moving towards the local minimum directly underneath its starting position. A subset of machine learning, which is itself a subset of, Currently, almost all organizations are using, The US will continue to hold the maximum market share for the next few years as it the home of many world’s top deep learning companies. This was accomplished by using larger mini-batch sizes that allows distributing the computational workload to 1000+ nodes. Intel Caffe: (http://github.com/intel/caffe/), revision b0ef3236528a2c7d2988f249d347d5fdae831236. ASGD requires more tuning of hyperparameters such as momentum, and requires more iterations to train. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly … They used the Places365* dataset to fine-tuned GoogleNet v1 and VGG-16 to produce the image embeddings used to compute the image similarities. There are various optimization algorithms that can be used to minimize the loss function such as gradient descent, or variants such as stochastic gradient descent, Adagrad, Adadelta, RMSprop, Adam, etc. Currently, almost all organizations are using machine learning algorithms to make their business functions effective and efficient. Comparing SGEMM and IGEMM performance we observe 2.3x and 3.4x improvement, respectively, over the previous Intel Xeon processor v4 generationc,e. High utilization requires that data be available when the execution units (EU) need it. Another deep learning processor appears in the ring: Grayskull from Tenstorrent A deep learning processor company has un-cloaked itself in Toronto – Tenstorrent, with a product called Grayskull. A cartoonish way to visualize this is shown in Figure 4 where the loss function with respect to the test dataset is slighted shifted from the loss function with respect to the training dataset. This technique also allowed SurfSARA and Intel researchers to scale to 512 2-socket Intel Xeon Platinum processors reducing the ResNet-50 time-to-train to 44 minutes. All these data was computed with fp32 precision. While the main focus of this article is on training, the first two factors also significantly improve inference performance. These include label and prepare data, choose an algorithm and train the model. Chong won the Intel Fellowship and then joined Intel 5 years ago. One strategy for communicating gradients is to appoint one node as the parameter server, which computes the sum of the node gradients, updates the model, and sends the updated weights back to each worker. By signing in, you agree to our Terms of Service. Andres Rodriguez. Hailo announced the Series A in June 2018 at $12.5 million (see Israel startup funded to develop deep learning processor ) and now Glory Ventures, joined by existing and other new investors, has taken the amount up to $21 million.. Andres received his PhD from Carnegie Mellon University for his research in machine learning. Deep learning systems also enable customized and self-service options for customers. For example, training ResNet-50 requires a total of about one exa (1018) single precision operations. This book describes deep learning systems: the algorithms, compilers, processors, and platforms to efficiently train and deploy deep learning models at scale in production. Dummy dataset was used. Artificial intelligence is one of the most exciting and attractive fields to get into. or Researchers at NVidia observed that the ratio of gradients to the weights for different layers within a model greatly varies. He has 13 years of experience working in AI. GCC 4.8.5, Intel MKL small libraries version 2018.0.20170425, interop parallelism threads set to 1 for alexnet, vgg benchmarks, 2 for googlenet benchmarks, intra op parallelism threads set to 56, data format used is NCHW, KMP_BLOCKTIME set to 1 for googlenet and vgg benchmarks, 30 for the alexnet benchmark. It describes the technology behind the processor as: “The first conditional execution architecture for artificial intelligence facilitating scalable deep learning. Kenneth has had the privilege of living through several digital revolutions in his lifetime. The researchers added some modifications that will be committed to the main Intel Optimized Caffe branch. OpenAI uses CPU to train evolution strategies (ES) achieving strong performance on RL benchmarks (Atari/MuJoCo). // Your costs and results may vary. Sharp minima do not generalize. 3.10.1. h. Platform: 2S Intel Xeon CPU E5-2699 v4 @ 2.20GHz (22 cores), HT enabled, turbo enable; BIOS: SE5C610.86B.01.01.0016.033120161139; Memory: 125.64GB, 12slots / 8192 MB / 2133 MHz; Disks: sda SanDisk SDSSDHII SSD 894.3GB,sdb INTEL SSDSC2BB48 SSD 447.1GB; OS: CentOS Linux-7.3.1611-Core; Kernel: 3.10.0-327.22.2.el7.x86_64. In Part 4, we present several commercial use cases at Intel's assembly and test factory, Facebook* services, deepsense.ai* reinforcement learning (RL) algorithms, Kyoto University drug design, Amazon* Web Services (AWS*) distributed machine learning applications, Clemson* University natural language processing, GE* Healthcare medical imaging, Aier* Eye Hospital Group and MedImaging* Integrated Solutions diabetic retinopathy, OpenAI* evolution strategies, and a variety of Apache* Spark* platforms using BigDL . In Part 1, we review the main hardware features of the Intel Xeon Scalable processors including compute and memory, and compare the performance of the Intel Xeon Scalable processors to previous generations of Intel Xeon processors for deep learning workloads. 3 Layer Deep LSTM for IMDB Sentiment Analysis. GCC 4.8.5, Intel MKL small libraries version 2017.0.2.20170110. g. Platform: 2S Intel Xeon CPU E5-2699 v3 @ 2.30GHz (18 cores), HT enabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 256GB DDR4-2133 ECC RAM. Principal Engineer and Big Data Chief Architect from Intel Software and Service Group (SSG), responsible for leading the development of advanced Big Data analytics (including distributed machine / deep learning), as well as collaborations with leading research labs (e.g., UC Berkeley AMPLab). In this article, we’ll briefly explain how deep learning works and introduce the best companies in 2020.Â. This magnitude of improvement is changing what is possible in industries such as health care, life sciences, financial services, scientific research, aerospace, automatic, manufacturing, and energy.". The [v-MP6000UDX processor from Videantis](http://www.videantis.com/products/deep-learning) is a scalable processor family … Using 8 Intel Xeon Platinum 8180 processor connected via 10 Gb Ethernet, training was completed within 1 hour. In practice, they’re responsible for feeding the models defined by data scientists. The Intel Xeon Scalable processors can support up to 28 physical cores (56 threads) per socket (up to 8 sockets) at 2.50 GHz processor base frequency and 3.80 GHz max turbo frequency, and six memory channels with up to 1.5 TB of 2,666 MHz DDR4 memory. This provides a significant performance boost over the previous 256-bit wide AVX2 instructions in the previous Intel Xeon processor v3 and v4 generations (formerly codename Haswell and Broadwell, respectively) for both training and inference workloads. This requires prefetching the data and reusing the data in cache instead of fetching that same data multiple times from main memory. // Performance varies by use, configuration and other factors. The processor can be reused and shared to accommodate deep neural networks that have various layer sizes and parameters. Intel and one if its partners successfully used Faster-RCNN* with Intel Optimized Caffe for the tasks of solar panel defect detection. Some of these primitives are inner products, convolutions, rectified linear units or ReLU, batch normalization, etc., along with functions necessary to manipulate tensors. are efficiently vectorized to the latest SIMD and parallelized across the cores. China UnionPay implemented a neural-network risk-control system utilizing Intel Xeon processors with BigDL and Apache Spark. Sensory is a Silicon Valley based AI and deep learning software company, focusing on providing products and services to improve the user experience through embedded machine learning technologies such as voice, vision, and natural language processing. i. Caffe: (http://github.com/intel/caffe/), revision f6d01efbe93f70726ea3796a4b89c612365a6341. Topology specs from https://github.com/dmlc/mxnet/tree/master/example/image-classification/symbols. The entire application pipeline was fully optimized to deliver significantly accelerated performance on Intel Xeon processors-based Hadoop cluster, with ~3.83x speedup compared to the same solution running on 20 K40 GPU cards. Your email address will not be published. Your email address will not be published. This technique (combined with the ones above) allowed them to increase the mini-batch size to 32K. The data layout is arranged consecutively in memory so that access in the innermost loops is as contiguous as possible avoiding unnecessary gather/scatter operations. To improve performance, graph optimizations may be required to keep conversion between different data layouts to a minimum as shown in Figure 3. MLSListings* and the Intel team also worked together to build an image similarity based house recommendation system in BigDL on Intel Xeon processors at Microsoft Azure.