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FPGA(性能) - メーカー・企業と製品の一覧

更新日: 集計期間:Aug 13, 2025~Sep 09, 2025
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FPGAの製品一覧

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Development case studies of large-scale computations using FPGAs.

Are you struggling with large-scale computations? Implement computation blocks on an FPGA on a PCI Express card.

We would like to introduce a case study of large-scale computation development using FPGA that our company has conducted. FPGAs excel at complex calculations with conditional branching and interrelated computations. Our customer was performing calculations using a GPU (NVIDIA Quadro RTX 8000), and when we compared the computation time on FPGA with that on GPU, we found a difference of about 14 to 15 times. 【Case Overview】 ■ Target Area: Accelerating computations unsuitable for GPUs using FPGA ■ System Configuration: Implementation of computation blocks on an FPGA on a PCI Express card ■ Performance Comparison between FPGA and GPU: A difference of about 14 to 15 times ■ Essential blocks for large-scale computation have already been developed ■ Compatible with both Windows OS and Linux OS *For more details, please refer to the PDF materials or feel free to contact us.

  • Embedded system design service

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FPGA : exLeaf

SD card-sized exLeaf-p with MachXO3LF, featuring 4 sets of PMOD-configured GPIO, totaling 16 GPIOs.

Realization of Ethernet connectivity for sensor devices with the optional technology cores e7tcpip and e7udpip licenses - High-reliability network performance Due to being non-microcontroller based, it does not go down even under DoS/DDoS attacks, and processing is done with a unique H/W circuit, making it unmodifiable. - High-speed processing / low time lag Direct input and output of data using FPGA without a microcontroller (Processing is possible for each event: packet command, trigger event, time event, loop event) Example: Data collection (20nsec cycle) → Data processing → Packet generation → Socket transmission (120μsec cycle) - Fast startup Starts up and begins processing in about 3msec (excluding Link-Up) - Power-saving / space-saving Composed only of network I/F and FPGA Link-Up: 180mW Link-Down: 3.12mW SD card size (32mm × 24mm)

  • 企業:HNS
  • 価格:Other
  • Embedded system design service
  • Circuit board design and manufacturing

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EFINIX TRION FPGA Embedded Board

ISX-EMB-EF002 is an Embedded Board Solution equipped with an Efinix Trion FPGA.

Equipped with Efinix's Trion T55 FPGA. Adopts an FPGA with MIPI D-PHY and a hard IP CSI-2 controller. An image sensor compatible with Raspberry Pi can be used. *1 Rich user logic is brought out to through holes. Equipped with USB 2.0 interface. FTDI's FT2232H is included. Includes an SD card slot. *1 The connected Image Sensor Module is not included. Please confirm the specifications before use.

  • Image Input Board

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Providing consistent services from reliable hardware design to manufacturing.

Opening up the design and manufacturing technology that has supported the reliability of outstanding Fujitsu products for many years - Fujitsu I-Network Systems.

Fujitsu I-Network Systems, Inc. (FI-NET) became independent from Fujitsu Limited last year in 2022 (due to a change in capital structure) and aims to leverage the hardware design and manufacturing technologies it has cultivated over many years to benefit new manufacturing partners beyond the Fujitsu Group. Additionally, the DX Smart Factory, developed through collaboration with Fujitsu and Fujitsu Laboratories and built on over 20 years of TPS activities, has become one of the top runners in Japan, attracting over 400 visitors annually. It can flexibly respond to various production varieties and volumes, including small-lot production, high-mix low-volume, and variant production, enabling the speedy provision of high-quality, highly reliable equipment. Based on the experience and know-how it has built, FI-NET aims to engage in new manufacturing projects. Through partnerships with client companies, it will provide high reliability and excellence, contributing to better manufacturing.

  • Circuit board design and manufacturing

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Technical Document "Deep Learning Using FPGA" *Currently Available for Free

Key points on tools for starting the design and implementation of machine learning-based applications!

Deep learning algorithms are gaining popularity in IoT applications at the edge due to their human-level accuracy in object recognition and classification. As a subset of machine learning, deep learning algorithms are inspired by the neural networks of the human brain, and by extending the concept of biological neural networks into machine learning, they effectively solve learning problems that were previously impossible. This document provides a detailed introduction to how Deep Neural Networks (DNN) operate and why FPGAs are becoming popular for DNN inference. Additionally, it includes the tools necessary to design and implement deep learning-based applications using FPGAs on Aldec's TySOM-3A-ZU19EG embedded development board, which features the largest FPGA from the Xilinx Zynq Ultrascale+ MPSoC family. We encourage you to make use of this information. *For more details, please refer to the PDF document or feel free to contact us.*

  • Embedded applications for mobile phones and PDAs
  • others

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VirtexUltraScale+iW-RainboW-G52M

Achieving the highest level of performance and functional integration at the 14nm/16nm FinFET node!

We would like to introduce the 'Virtex UltraScale+ FPGA SOM iW-RainboW-G52M' that we handle. AMD's third-generation 3D IC adopts Stacked Silicon Interconnect (SSI) technology to achieve excellent performance beyond Moore's Law, providing the highest level of signal processing performance and serial I/O bandwidth to meet stringent design requirements. Additionally, it offers registered wiring lines between dies, enabling operation above 600MHz with richer and more flexible clocking, thus providing a virtual monolithic design environment. 【Overview and Features】 ■ Xilinx Virtex UltraScale+ FPGA with B2104 package ■ Dual 4GB FPGA DDR4 with ECC (64bit + 8bit) ■ 2GB DDR4 for FPGA (32bit) ■ 76 FPGA GTY High-Speed Transceiver channels up to 32.75Gbps *For more details, please download the PDF or feel free to contact us.

  • Embedded Board Computers

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Agilex7 SoC FPGA iW-RainboW-G43M

Design optimization feature! Significant improvement in fMAX performance compared to the previous generation high-end Intel FPGAs.

We would like to introduce the 'Agilex 7 SoC FPGA SOM iW-RainboW-G43M' that we handle. It features an enhanced 200G (half-duplex communication) encryption core with MACsec IP supporting AES-GCM encryption/decryption, protecting network traffic. With enhanced bfloat16 and up to 25 TFLOPS of digital signal processing (DSP) performance (FP16), it achieves high performance per watt. 【Features (partial)】 ■ Quad ARM Cortex A53 Processor ■ R31B/R31C Package With 4 x F-Tile ■ 32GB eMMC (Expandable) ■ 8GB DDR4 with ECC for HPS (64bit + 8bit) ■ 2 x 8GB DDR4 with ECC for FPGA (64bit + 8bit) *For more details, please download the PDF or feel free to contact us.

  • Embedded Board Computers

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Agilex5 SoC FPGA iW-RainboW-G58M

Securing class-leading lead times! E-series FPGAs equipped with Intel 7 technology.

We would like to introduce the 'Agilex 5 SoC FPGA SOM iW-RainboW-G58M' that we handle. It achieves excellent power efficiency performance with 1.6 times the performance per watt compared to competing products with 16nm FPGA, improving HPS energy efficiency with the first asymmetric Arm application processor cluster. With a wide range of IP sets and connection options covering a broad density range (50KLE-656KLE), it meets the diverse application needs and allows scaling of device size to accommodate various I/O standards for edge and embedded sensors that can be configured with a single FPGA device. [Features (partial)] ■ Agilex 5 E-Series family with B32A Package ■ Dual-core 64-bit Arm Cortex-A76 up to 1.8 GHz ■ Dual-core 64-bit Arm Cortex-A55 up to 1.5 GHz *For more details, please download the PDF or feel free to contact us.

  • Embedded Board Computers

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