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.
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:
ShuraCore team specializes in using the following frameworks and tools:
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.
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 is running. This compilation is also known as dynamic compilation. The advantage of a JIT compilation strategy is that it has complete knowledge of the target architecture on which the
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: lexical, syntactic, and semantic analysis. Front-end compilers perform the following functions: Character table management; Data structure management; Analyzing the source code and displaying information (location, type, and scope) associated with
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 a middle-end compiler can range from a function to full software. ShuraCore development team specializes in the following areas of middle-end compilers: Optimizers Analyzers Optimizers Optimizing compilers are the backbone
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 process. The design consists of several phases that are performed to form a binary file for the target architecture. When designing a back-end compiler, it is common to distinguish the
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
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 these compilers is for hardware. An example of hardware is a field-programmable gate array (FPGA) or structural application-specific integrated circuit (ASIC). Compilers are called hardware compilers because the source code
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 platform or virtualizes a specific venue and creates environments that isolate programs and even operating systems from each other. A virtual machine also refers to the specification of some computing
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 advantage of stepping through the code without the need for compilation, which can be useful for running it on embedded platforms. Our company is engaged in porting interpreters for any
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 process. The most common task of the HLS compiler is to generate the HDL group languages (Verilog or VHDL) from the source code of the high-level languages (C/C++). Many modern implementations of HLS compilers
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 machines. The word “LLVM” itself is not an acronym. It is the full name of the project. LLVM and the GNU Compiler Collection (GCC) are compilers. The difference is that