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 principle is that machines receive data and “learn” from it. It is currently the most promising AI-powered business tool. Machine Learning systems quickly apply knowledge gained from training on large datasets, enabling them to excel in tasks such as face recognition, speech recognition,

Deep Learning

Deep Learning

Deep learning is a subset of machine learning. It uses machine learning techniques to solve business problems by applying neural networks to choose the best adaptation model in industrial enterprise management. Deep Learning can be expensive and requires vast amounts of training data. This is because many parameters need to be tuned for learning algorithms

Edge AI

Edge computing consists of several methods that bring data collection, analysis, and processing to the network’s edge. It means that computing power and data storage is where the actual data collection takes place. Edge AI devices include smart speakers, smartphones, laptops, robots, self-driving cars, drones, and surveillance cameras that use video analytics.  Edge AI Benefits:

NVIDIA Jetson

The Jetson family of modules all use the same NVIDIA CUDA-X™ software and support cloud-native technologies like containerization and orchestration to build, deploy, and manage AI at the edge. With Jetson, we can accelerate all modern AI networks, easily implement new features, and use the same software for different products and applications. With Jetson, we

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

FPGA with Nvidia Jetson for AI solutions. Nvidia Jetson Nano, Jetson TX2, Jetson Xavier NX, Jetson AGX Xavier AI platforms in FPGA developing

FPGA with Nvidia Jetson

NVIDIA Jetson is the leading Edge AI computing platform used by over a million developers and companies worldwide. With cloud support across all NVIDIA Jetson products, intelligent machine makers and AI-powered embedded systems developers are empowered to develop and deploy high-tech software functions to edge devices in areas such as industrial automation and robotics, smart cities, and smart agriculture, Industrial Internet of Things,

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:

Compiler Design

Compiler Design Services

ShuraCore specializes in implementing new and modern ports: GCC, GDB, GNU libraries, Binutils, LLDB, LLVM utilities, and libraries. We are engaged in the optimization and adaptation of existing compilers for any hardware platform. ShuraCore team provides a full range of services for the development of compilers and interpreters of the following types: JIT and AOT

IP Cores

Intellectual Property (IP) Core is a block of logic or data used to create FPGA or particular purpose integrated circuit solutions. As a critical element of design reuse, IP cores are part of a growing trend in the Electronic Design Automation (EDA) industry. PCIe, SATA, NVMe GbE, 10G, 40G, Communication controllers VGA, HDMI, DVI, Video controllers GPIO,

Our development team uses the following software processors in FPGA design: RISC-V (Rocket, VexRiscv, PicoRV), NIOS ||, Microblaze, etc.

Software Processors

When designing embedded systems, FPGA often requires some form of a controller in the system. This controller can be a simple microcontroller or a full-fledged microprocessor running a Linux or RTOS operating system. Solutions with a software processor and software core are fully implemented in logical FPGA primitives. Our development team uses the following software

ShuraCore uses SystemVerilog/Verilog/VHDL, C/C++, Chisel, SpinalHDL, MyHDL, and TCL for FPGA Software Development. Programming Language.

Programming Languages

The programming language for FPGA is commonly referred to as hardware description language because it is used to describe or design hardware. For FPGA programming, we use classic HDL languages and high-level languages. SystemVerilog/Verilog/VHDL C/C++ Chisel, SpinalHDL, MyHDL TCL Very often, our customers need to develop accompanying software along with the development of embedded software. The

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 team of specialists uses proven tools that effectively develop and test software for FPGA, allowing you to speed up and optimize the programming process. Our company uses CI/CD and FPGA software testing tools. CI/CD for FPGA projects Vivado/System Generator/Vitis/Vivado HLS Quartus/Intel HLS Compiler Matlab/Simulink 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

FPGA with Nvidia Jetson for AI solutions. Nvidia Jetson Nano, Jetson TX2, Jetson Xavier NX, Jetson AGX Xavier AI platforms in FPGA developing

FPGA with Nvidia Jetson

NVIDIA Jetson is the leading Edge AI computing platform used by over a million developers and companies worldwide. With cloud support across all NVIDIA Jetson products, intelligent machine makers and AI-powered embedded systems developers are empowered to develop and deploy high-tech software functions to edge devices in areas such as industrial automation and robotics, smart cities, and smart agriculture, Industrial Internet of Things,

DevOps for FPGA

Our company uses advanced technologies DevOps for FPGA, which allow us to develop projects on time and optimize risks when designing FPGA. Improved deployment frequency Faster time to market Sometimes it can take several hours to create a project. We use DevOps so that our specialists always develop projects and do not wait until the

FPGA Verification

Verification is the verification of the device’s model being developed, designed by a team of specialists in one of the hardware description languages, based on the technical task. Verification engineers must conclude that the developed model complies with the declared specification and can be applied at further stages of the final digital device design.Verification is

HLS for FPGA

High-Level Synthesis (HLS) is used to create digital devices using high-level languages. The main goal of HLS products is to simplify the FPGA design process for a developer who is familiar with programming in high-level languages ​​such as C++, Rust, etc. The practical application of FPGA often causes difficulties for Java, .Net programmers, etc. tasks: it becomes necessary to understand how

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