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Hardware Acceleration Using Functional Languages
Hodaňová, Andrea ; Kadlček, Filip (referee) ; Fučík, Otto (advisor)
The aim of this thesis is to research how the functional paradigm can be used for hardware acceleration with an emphasis on data-parallel tasks. The level of abstraction of the traditional hardware description languages, such as VHDL or Verilog, is becoming to low. High-level languages from the domains of software development and modeling, such as C/C++, SystemC or MATLAB, are experiencing a boom for hardware description on the algorithmic or behavioral level. Functional Languages are not so commonly used, but they outperform imperative languages in verification, the ability to capture inherent paralellism and the compactness of code. Data-parallel task are often accelerated on FPGAs, GPUs and multicore processors. In this thesis, we use a library for general-purpose GPU programs called Accelerate and extend it to produce VHDL. Accelerate is a domain-specific language embedded into Haskell with a backend for the NVIDIA CUDA platform. We use the language and its frontend, and create a new backend for high-level synthesis of circuits in VHDL.
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Automatic lifting of expressions for typed functional languages
Smrž, Roman ; Pudlák, Petr (advisor) ; Hric, Jan (referee)
In typed functional programming there is often the need for combining pure and monadic (or other effectful) computations, but the required lifting must be done manually by the programmer and may result in cluttered code. This thesis explores ways to allow the compiler to perform this task automat- ically. Several possible approaches are described, where the final one reduces the task to solving a system of linear diophantine equations. Apart from monads, the described method is also considered for the case of applicative functors as another abstraction to represent effectful operations. 1
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Hardware Acceleration Using Functional Languages
Hodaňová, Andrea ; Kadlček, Filip (referee) ; Fučík, Otto (advisor)
The aim of this thesis is to research how the functional paradigm can be used for hardware acceleration with an emphasis on data-parallel tasks. The level of abstraction of the traditional hardware description languages, such as VHDL or Verilog, is becoming to low. High-level languages from the domains of software development and modeling, such as C/C++, SystemC or MATLAB, are experiencing a boom for hardware description on the algorithmic or behavioral level. Functional Languages are not so commonly used, but they outperform imperative languages in verification, the ability to capture inherent paralellism and the compactness of code. Data-parallel task are often accelerated on FPGAs, GPUs and multicore processors. In this thesis, we use a library for general-purpose GPU programs called Accelerate and extend it to produce VHDL. Accelerate is a domain-specific language embedded into Haskell with a backend for the NVIDIA CUDA platform. We use the language and its frontend, and create a new backend for high-level synthesis of circuits in VHDL.
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