the proceedings contain 55 papers. the topics discussed include: leveraging FPGA runtime reconfigurability to implement multi-hash-chain proof-of-work;augmenting HLS with zero-overhead application-specific address map...
ISBN:
(纸本)9781665483322
the proceedings contain 55 papers. the topics discussed include: leveraging FPGA runtime reconfigurability to implement multi-hash-chain proof-of-work;augmenting HLS with zero-overhead application-specific address mapping for OPTANE DCPMM;resource sharing for verified high-level synthesis;FPGA accelerator for homomorphic encrypted sparse convolutional neural network inference;an FPGA accelerator for genome variant calling;a dual-mode similarity search accelerator based on embedding compression for online cross-modal image-text retrieval;software defined optical time-domain reflectometer;reverse engineering neural network folding with remote FPGA power analysis;high-rate machine learning for forecasting time-series signals;and characterization of side channels on FPGA-based off-the-shelf boards against automated attacks.
the proceedings contain 54 papers. the topics discussed include: Pharos: a performance monitor for multi-FPGA systems;extending HLS with high-level descriptive language for configurable algorithm-level spatial structu...
ISBN:
(纸本)9780738126739
the proceedings contain 54 papers. the topics discussed include: Pharos: a performance monitor for multi-FPGA systems;extending HLS with high-level descriptive language for configurable algorithm-level spatial structure design;ONT-X: an FPGA approach to real-time portable genomic analysis;TwinDNN: a tale of two deep neural networks;a general video processing framework on edge computing FPGAs;scalable FPGA median filtering via a directional median cascade;scheduling persistent and fully cooperative instructions;time-domain FPGA power delivery network characterization methodology;trusted configuration in cloud FPGAs;and FA-LAMP: FPGA-accelerated learned approximate matrix profile for time series similarity prediction.
the proceedings contain 65 papers. the topics discussed include: an automated tool for design space exploration of matrix vector multiplication (MVM) kernels using OpenCL based implementation on FPGAs;update latency o...
ISBN:
(纸本)9781728158037
the proceedings contain 65 papers. the topics discussed include: an automated tool for design space exploration of matrix vector multiplication (MVM) kernels using OpenCL based implementation on FPGAs;update latency optimization of packet classification for SDN switch on FPGA;realization of quantized neural network for super-resolution on PYNQ;FPGA implementation of post-quantum DME cryptosystem;explore efficient LUT-based architecture for quantized convolutional neural networks on FPGA;high-throughput convolutional neural network on an FPGA by customized jpeg compression;optimizing reconfigurable recurrent neural networks;accelerating proximal policy optimization on CPU-FPGA heterogeneous platforms;evaluating low-memory GEMMs for convolutional neural network inference on FPGAs;and hardware architecture of a number theoretic transform for a bootstrappable RNS-based homomorphic encryption scheme.
the proceedings contain 70 papers. Topics discussed include: OpenCL kernel vectorization on the CPU, GPU, and FPGA:a case study with frequent pattern compression;FASE: FPGA acceleration of secure function evaluation;m...
ISBN:
(纸本)9781728111315
the proceedings contain 70 papers. Topics discussed include: OpenCL kernel vectorization on the CPU, GPU, and FPGA:a case study with frequent pattern compression;FASE: FPGA acceleration of secure function evaluation;monobit wideband receiver with integrated dithering in FPGA;cost-effective energy monitoring of a Zynq-based real-time system including dual gigabit ethernet;deep packet inspection in FPGAs via approximate nondeterministic automata;module-per-object: a human-driven methodology for C++-based high-level synthesis design;model-extraction attack against FPGA-DNN accelerator utilizing correlation electromagnetic analysis;rethinking integer divider design for FPGA-based soft-processors;and high precision, high performance FPGA adders.
the proceedings contain 53 papers. the topics discussed include: improved lightweight implementations of CAESAR authenticated ciphers;high-throughput lossless compression on tightly coupled CPU-FPGA platforms;inheriti...
ISBN:
(纸本)9781538655221
the proceedings contain 53 papers. the topics discussed include: improved lightweight implementations of CAESAR authenticated ciphers;high-throughput lossless compression on tightly coupled CPU-FPGA platforms;inheriting software security policies within hardware IP components;ReBNet: residual binarized neural network;FlexiGAN: an end-to-end solution for FPGA acceleration of generative adversarial networks;understanding performance differences of FPGAs and GPUs;concurrency-aware thread scheduling for high-level synthesis;high-level synthesis of FPGA circuits with multiple clock domains;latte: locality aware transformation for high-level synthesis;improving the effectiveness of TMR designs on FPGAs with SEU-aware incremental placement;and microscope on memory: MPSoC-enabled computer memory system assessments.
Alongside traditional look-up tables (LUTs), Digital Signal Processors (DSPs) and block memories (BRAMs), modern FPGAs include many specialised processing elements such as CPU cores and AI accelerators [1] . Applicati...
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ISBN:
(纸本)9781665483322
Alongside traditional look-up tables (LUTs), Digital Signal Processors (DSPs) and block memories (BRAMs), modern FPGAs include many specialised processing elements such as CPU cores and AI accelerators [1] . Applications wish to maximise performance by using all available processing elements, but additional work is required to support and manage their different implementations and behaviours.
Binarised neural networks (BNNs) have attracted research interest for embedded deep learning applications. BNNs are well suited to FPGA implementation since BNNs have small memory utilisations and make use of many bin...
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ISBN:
(纸本)9781665483322
Binarised neural networks (BNNs) have attracted research interest for embedded deep learning applications. BNNs are well suited to FPGA implementation since BNNs have small memory utilisations and make use of many binary logic operations in parallel. Moreover, the FPGA acceleration of BNNs has very high energy efficiency and performance [1] , making FPGA-based BNNs attractive for implementing neural network capability in power-constrained satellite systems.
thanks to the flexibility of FPGAs and their widespread adoption in the cloud, they have become attractive solutions for the acceleration of resource- and memory-intensive database workloads. Complex pipeline-breaking...
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ISBN:
(纸本)9781665483322
thanks to the flexibility of FPGAs and their widespread adoption in the cloud, they have become attractive solutions for the acceleration of resource- and memory-intensive database workloads. Complex pipeline-breaking operators (e.g., aggregation, join) often constitute most of the execution time of the queries involved in these workloads. A popular approach in processing these operators is by pre-sorting the input, as they become single-pass algorithms on sorted tables [1] .
Recently, FPGAs have been coupled with processors to form System on Chip (SoC) devices. SoC design is challenging as it combines both hardware and software elements. A variety of tools must therefore be utilised to de...
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ISBN:
(纸本)9781665483322
Recently, FPGAs have been coupled with processors to form System on Chip (SoC) devices. SoC design is challenging as it combines both hardware and software elements. A variety of tools must therefore be utilised to design for these components, each requiring different knowledge.
Data centers provide good environments for distributed computing as they are easily accessible and may have low-latency communication between nodes [1] ; often, however, performance is limited by network bandwidth. th...
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ISBN:
(纸本)9781665483322
Data centers provide good environments for distributed computing as they are easily accessible and may have low-latency communication between nodes [1] ; often, however, performance is limited by network bandwidth. these network bottlenecks drive the need for alternative communication resources to improve performance of large-scale applications. SmartNICs [2] – [4] have been introduced to perform the same tasks of standard NICs, but contain additional resources to allow for network function optimization with additional hardware. Adoption of SmartNICs continues to increase as a means to accelerate network functions and offload packet processing tasks away from CPU resources [5] – [13] .
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