MLIR

The MLIR (Multilevel Intermediate View) project is a new approach to building a reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain-specific compilers, and merge existing compilers. MLIR is designed for hybrid intermediate representation (IR) to support multiple different requirements in a single infrastructure.

The MLIR project defines a standard IR that brings together the infrastructure needed to run high-performance machine learning models in TensorFlow and similar ML environments. This project includes the application of HPC techniques and the integration of search algorithms such as reinforcement learning. MLIR aims to reduce the cost of new hardware implementation and improve usability for existing TensorFlow users.

MLIR_en

The MLIR (Multilevel Intermediate View) project is a new approach to building a reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain-specific compilers, and merge existing compilers. MLIR is designed for hybrid intermediate representation (IR) to support multiple different requirements in a single infrastructure.

The MLIR project defines a standard IR that brings together the infrastructure needed to run high-performance machine learning models in TensorFlow and similar ML environments. This project includes the application of HPC techniques and the integration of search algorithms such as reinforcement learning. MLIR aims to reduce the cost of new hardware implementation and improve usability for existing TensorFlow users.

MLIR brings together the infrastructure for high-performance ML models in TensorFlow. The TensorFlow ecosystem contains several compilers and optimizers that operate at multiple software and hardware stack levels. We expect the gradual adoption of MLIR to simplify every aspect of this stack. ShuraCore uses the MLIR project to design compilers for the following hardware platforms:

  • CPU
  • FPGA
  • GPU
  • TPU

ShuraCore team specializes in using the following frameworks and tools:

  • TensorFlow
  • Caffe
  • PyTorch
  • LLVM
  • CUDA
  • OpenCL
AI Development

Compiler Design Services

AsmJit, MIR, nj, NanoJIT, LLVM - MCJIT, Libraries, OpenMP, OpenACC, LLVM, C++, Rust, bison, flex, yacc, AntLR

Compiler Design Services

CPU, FPGA, GPU, TPU, TensorFlow, Caffe, PyTorch, LLVM, CUDA, OpenCL, WASM
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ShuraCore specializes in implementing new and modern ports: GCC, GDB, GNU libraries, Binutils, LLDB, LLVM utilities, and libraries. In addition, we are engaged in the optimization and adaptation of existing compilers for any hardware platform. Finally, the ShuraCore team provides a full range of services for the development of compilers and interpreters.

We also work in the following areas: development of SDK, virtual machines, obfuscators, and code deobfuscators for our clients. We port debuggers and simulators to new hardware platforms, write high-speed optimizations. Our team also develops compilers for neural and tensor processors. ShuraCore creates developer tools based on the LLVM framework.

JIT and AOT

JIT (Just-in-time) compilers are used to improve the performance of interpreted programs. JIT compilation is about compiling a program into its code while the program

Front-end Compilers

The front-end compiler, analyzing the source code, creates an internal representation of the program – an intermediate representation (IR). The front-end consists of three phases:

Middle-end Compilers

Middle-end compilers are used to optimize and analyze software source code. The range of compiler analysis and optimization has many functional differences. The scope of

Back-end Compilers

The back-end compiler is responsible for specific optimization for the processor architecture and code generation for a particular architecture. Back-end design is not a trivial

MLIR

The MLIR (Multilevel Intermediate View) project is a new approach to building a reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve

Hardware Compilers

Hardware compilers, or synthesis tools, are compilers whose output is a description of a hardware configuration instead of a sequence of instructions. The output of

Virtual Machine

A virtual machine is a software or hardware system that emulates a particular platform’s hardware and executes programs for a target platform on a host

AST and bytecode interpreters

An interpreter is a translator whose task is to perform line-by-line analysis, process and execute the program’s source code or request. The interpreter has the

HLS Compilers

HLS (High-Level Synthesis) compilers are used to create digital devices using high-level languages. The main goal of HLS products is to simplify the FPGA and ASIC design

LLVM

The LLVM Project is a collection of modular and reusable compiler and toolchain technologies. Despite its name, LLVM has little to do with traditional virtual

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