FPGAs for Artificial Intelligence (AI) - ShuraCore | FPGA Design Services

FPGAs for Artificial Intelligence (AI)

With the growing popularity of using machine learning algorithms to extract and process information from raw data, there was a race between FPGA and GPU vendors to offer an HW platform that quickly and efficiently runs resource-intensive machine learning algorithms. Since deep learning is used in most advanced machine learning applications, it is considered the main point of comparison.

FPGA deserves a place among GPU and processor-based artificial intelligence chips for big data and machine learning. They show great potential for accelerating AI workloads, in particular inference. The main advantages of using FPGA to accelerate machine learning and deep learning processes are their flexibility, configurable parallelism, and reprogram for different purposes.

Advantages of FPGA technology:

  1. Flexibility. The ability to reprogram for various purposes is one of the main advantages of FPGA technology. For AI solutions, individual blocks or the entire circuit can be reprogrammed according to the requirements of a particular data processing algorithm.
  2. Parallelism. FPGA can handle multiple workloads while maintaining high application performance and can adapt to changing workloads by switching between various programs.
  3. Reduced latency. The FPGA has a higher memory bandwidth than a conventional GPU, which reduces latency and allows large amounts of data to be processed in real-time.
  4. Energy efficiency. Machine learning and deep learning are resource-intensive solutions. But it is possible to provide a high level of performance for low-power machine learning applications using FPGAs.
  5. Functional safety. FPGAs are used in industries where functional safety plays a critical role, such as automation, avionics, and defense. Thus, FPGAs have been designed to meet the security requirements of a wide range of applications, including ADAS. As such, the Xilinx Zynq®-7000 and Ultrascale + TM MPSoC devices are designed to support security-critical applications such as ADAS.
  6. Programming. The flexibility of FPGAs is achieved due to the complexity of reprogramming the circuit. The use of HLS allows us to speed up the process of AI processing on FPGAs.

AI Development Services

Tensorflow, PyTorch, Keras, Caffe, Darknet, MxNet

AI Development Services

Xilinx Machine Learning (ML) Suite, NVIDIA CUDA-X AI and CUDA, RadeonML and ROCm, OpenCL and OpenMP, OpenVINO
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Artificial intelligence, machine learning, and deep learning are integral parts of many enterprises, factories, and complex software. This terminology is often used synonymously. Artificial intelligence is making huge strides forward – from advances in self-driving vehicles and the ability to beat humans in games to automated customer service and full automation and decision-making in various industries.

Artificial Intelligence is a cutting-edge technology that is poised to revolutionize your business. Artificial intelligence development is also driving software development services, embedded software, IoT, and IIOT applications. Software developers are currently exploring new ways of programming that are more prone to deep learning and machine learning. ShuraCore provides software development services based on machine learning, reinforcement learning, and deep learning. To solve business problems, we use the following technologies, frameworks, and approaches:

Machine Learning

Machine Learning

Machine Learning is one of the branches of artificial intelligence. The basic

Deep Learning

Deep Learning

Deep learning is a subset of machine learning. It uses machine learning

Edge AI

Edge computing consists of several methods that bring data collection, analysis, and

NVIDIA Jetson

The Jetson family of modules all use the same NVIDIA CUDA-X™ software

FPGA Design Services

RISC-V (Rocket, VexRiscv, PicoRV), PCIe, SATA, NVMe, USB, GbE, 10G, 40G, Communication controllers, VGA, HDMI, DVI, Video controllers, GPIO, I2C, I3C, SPI, QSPI, TileLink, AXI, AXIS, Avalon, Wishbone

FPGA Design Services

SystemVerilog/Verilog/VHDL, C/C++, Chisel, SpinalHDL, MyHDL, TCL, CI/CD for FPGA projects, Vivado/System Generator/Vitis/Vivado HLS, Quartus/Intel HLS Compiler
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Our team is an expert in FPGA design. We maintain our service at a high level, which allows us to provide comprehensive solutions for FPGA design for various systems. Our company keeps pace with the times, has extensive experience in existing FPGA technologies. Using multiple technologies, practical and theoretical knowledge, experience in developing individual solutions for FPGA, we create a unique customer solution. If you need our expertise in developing or creating a unique FPGA solution, we will be happy to help you.

When implementing a project using FPGA technologies, the device’s budget, time, development complexity, performance requirements, and business logic are considered. ShuraCore team has deep industry expertise and high technical qualifications in FPGA solution development, which allows us to participate in various projects, not being limited to any one area of development. Below is our experience with multiple technologies for FPGA:

IP Cores

Intellectual Property (IP) Core is a block of logic or data used

We use CI/CD for FPGA projects, Vivado/System Generator/Vitis/Vivado HLS, Quartus/Intel HLS Compiler, Matlab/Simulink.Tools.

Tools

Software development, like any other field of activity, requires specific tools. Our

DevOps for FPGA

Our company uses advanced technologies DevOps for FPGA, which allow us to

FPGA Verification

Verification is the verification of the device’s model being developed, designed by

HLS for FPGA

High-Level Synthesis (HLS) is used to create digital devices using high-level languages.

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