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Abstract:Convolutional neural networks (CNNs) are widely used in modern applications for their versatility and high classification accuracy. Field-programmable gate arrays (FPGAs) are considered to be suitable platforms for CNNs based on their high performance, rapid development, and reconfigurability. Although many studies have proposed methods for implementing high-performance CNN accelerators on FPGAs using optimized data types and algorithm transformations, accelerators can be optimized further by investigating more efficient uses of FPGA resources. In this paper, we propose an FPGA-based CNN accelerator using multiple approximate accumulation units based on a fixed-point data type. We implemented the LeNet-5 CNN architecture, which performs classification of handwritten digits using the MNIST handwritten digit dataset. The proposed accelerator was implemented, using a high-level synthesis tool on a Xilinx FPGA. The proposed accelerator applies an optimized fixed-point data type and loop parallelization to improve performance. Approximate operation units are implemented using FPGA logic resources instead of high-precision digital signal processing (DSP) blocks, which are inefficient for low-precision data. Our accelerator model achieves 66% less memory usage and approximately 50% reduced network latency, compared to a floating point design and its resource utilization is optimized to use 78% fewer DSP blocks, compared to general fixed-point designs.Keywords: convolutional neural network; FPGA; high-level synthesis; accelerator
Fpga Based Accelerators For Financial Applications Pdf Free
Amazon EC2 F1 instances provide up to 100X acceleration compared to CPUs for a diverse set of compute-bound applications. Customers can discover, test and deploy custom accelerators directly from the AWS Marketplace to accelerate their compute pipelines with ease. There is no need to know how to program FPGAs, as F1 based products developed by F1 technology partners are packaged as any other EC2 instance software.
InAccel FPGA-Accelerated ML (AML) Suite provides a set of accelerators that run on Amazon EC2 F1 instances for ML applications. Applications developed using popular frameworks such as Apache Spark, Scikit-learn, and Keras can be accelerated using InAccel FPGA-Accelerated ML Suite. It is shipped as a fully integrated AMI that can be used to accelerate deep learning and machine learning algorithms for classification and clustering. InAccel's novel "FPGA Resource Manager" Docker container handles all the available FPGA resources allowing developers to seamlessly scale their containerized workloads to multiple F1 instances.
The FPGA segment is projected to attain the highest CAGR of 26.4% in the forecast period. Cloud service providers like Microsoft, Amazon, Alibaba, Baidu, and Tencent have been adopting the FPGAs as a reconfigurable heterogeneous processing asset. Moreover, improvements in architecture, programming paradigms, and security are expected to result in a wider variety of applications for FPGA-based cloud deployment.
The enterprise interface segment is projected to grow at a highest CAGR of 25.7%, over the forecast period. Hyper-scale cloud-based organizations like Amazon.com, Inc., Google, and Facebook are increasingly targeting digital transformation to create similar kinds of cloud-native applications likely to contribute to the growth of the segment. Also, many enterprises are adopting cloud migration and cloud-first policies by increasing their cloud spend and workload volumes.
The competitive landscape of the data center accelerator market is fragmented, having numerous local and global data center accelerator companies. The key participants are adopting advanced technologies to offer better solutions to their customers. Moreover, companies are launching a new line of products and services for advanced applications such data analytics and AI. In November 2020, Advanced Micro Devices, Inc. launched AMD Instinct MI100 Accelerator and next-generation AMD EPYC processors dedicated to High-Performance Computing (HPC) workloads. Combining instinct accelerators and AMD EPYC processors with critical application software and development tools enables AMD to deliver redefine performance for HPC workloads. Some prominent players in the global data center accelerator market are:
We present uKharon, a microsecond-scale membership service that detects changes in the membership of applications and lets them failover in as little as 50us. uKharon consists of (1) a multi-level failure detector, (2) a consensus engine that relies on one-sided RDMA CAS, and (3) minimal-overhead membership leases, all exploiting RDMA to operate at the microsecond scale. We showcase the power of uKharon by building uKharon-KV, a replicated Key-Value cache based on HERD. uKharon-KV processes PUT requests as fast as the state-of-the-art and improves upon it by (1) removing the need for replicating GET requests and (2) bringing the end-to-end failover down to 53us, a 10x improvement.
Distributed big-data analytics heavily rely on high-level languages like Java and Scala for their reliability and versatility. However, those high-level languages also create obstacles for data exchange. To transfer data across managed runtimes like Java Virtual Machines (JVMs), objects should be transformed into byte arrays by the sender (serialization) and transformed back into objects by the receiver (deserialization). The object serialization and deserialization (OSD) phase introduces considerable performance overhead. Prior efforts mainly focus on optimizing some phases in OSD, so object transformation is still inevitable. Furthermore, they require extra programming efforts to integrate with existing applications, and their transformation also leads to duplicated object transmission. This work proposes Zero-Change Object Transmission (ZCOT), where objects are directly copied among JVMs without any transformations. ZCOT can be used in existing applications with minimal efforts, and its object-based transmission can be used for deduplication. The evaluation on state-of-the-art data analytics frameworks indicates that ZCOT can greatly boost the performance of data exchange and thus improve the application performance by up to 23.6%.
As machine learning (ML) techniques are applied to a widening range of applications, high throughput ML inference serving has become critical for online services. Such ML inference servers with multiple GPUs pose new challenges in the scheduler design. First, they must provide a bounded latency for each request to support a consistent service-level objective (SLO). Second, they must be able to serve multiple heterogeneous ML models in a system, as cloud-based consolidation improves system utilization.To address the two requirements of ML inference servers, this paper proposes a new inference scheduling framework for multi-model ML inference servers. The paper shows that with SLO constraints,GPUs with growing parallelism are not fully utilized for ML inference tasks. To maximize the resource efficiency of GPUs, a key mechanism proposed in this paper is to exploit hardware supportfor spatial partitioning of GPU resources. With spatio-temporal sharing, a new abstraction layer of GPU resources is created with configurable GPU resources. The scheduler assigns requests to virtual GPUs, called gpulets, with the most effective amount of resources. The scheduler explores the three-dimensional search space with different batch sizes, temporal sharing, and spatial sharing efficiently.To minimize the cost for cloud-based inference servers, the framework auto-scales the required number of GPUs for a given workload. To consider the potential interference overheads when two ML tasks are running concurrently by spatially sharing a GPU, the scheduling decision is made with an interference prediction model. Our prototype implementation proves that the proposed spatio-temporal scheduling enhances throughput by 61.7% on average compared to the prior temporal scheduler, while satisfying SLOs.
We implement Privbox based on Linux and LLVM. Our evaluation on x86 (Intel Skylake) hardware shows that Privbox (1) speeds up system call invocation by 2.2 times; (2) can increase throughput of I/O-threaded applications by up to 1.7 times; and (3) can increase the throughput of real-world workloads such as Redis by up to 7.6% and 11%, without and with SPAP, respectively.
Pridwen is a framework that selectively applies essential SCA countermeasures when loading an SGX program based on the configurations of the target execution platform. Pridwen allows a developer to deploy a program in the form of WebAssembly (Wasm). Upon receiving a Wasm binary, Pridwen probes the current hardware configuration, synthesizes a program (i.e., a native binary) with an optimal set of countermeasures, and validates the final binary. Pridwen supports both software-only and hardware-assisted countermeasures, and our evaluations show Pridwen efficiently, faithfully synthesizes multiple benchmark programs and real-world applications while securing them against multiple SCAs.
The prosperity of AI and edge computing has pushed more and more well-trainedDNN models to be deployed on third-party edge devices to compose mission-criticalapplications. This necessitates protecting model confidentiality at untrusted devices, andusing a co-located accelerator(e.g., GPU) to speed up model inference locally. Recently, the community has soughtto improve the security with CPU trusted executionenvironments (TEE). However, existing solutions eitherrun an entire model in TEE, suffering from extremely highinference latency, or take a partition-based approach to handcraft partial model via parameter obfuscation techniques to run on an untrustedGPU, achieving lower inference latency at the expense of both the integrity ofpartitioned computations outside TEE and accuracy of obfuscated parameters.
The conventional file system provides a hierarchical namespace by structuring it as a directory tree. Tree-based namespace structure leads to inefficient file path walk and expensive namespace tree traversal, underutilizing ultra-low access latency and good sequential performance provided by non-volatile memory systems. This paper proposes FlatFS, a NVM file system that features a flat namespace architecture while provides a compatible hierarchical namespace view. FlatFS incorporates three novel techniques: coordinated file path walk model, range-optimized NVM-friendly B^r tree, and write-optimized compressed index key layout, to fully exploit flat namespace structure to improve file system namespace performance on high-performance NVMs. Evaluation results demonstrate that FlatFS achieves significant performance improvements for metadata-intensive benchmarks and real-world applications compared to other file systems. 350c69d7ab