VHDL Tutorial
The purpose of this tutorial is to describe the modeling language VHDL. VHDL includes facilities for describing logical structure and function of digital systems at a number of levels of abstraction, from system level down to the gate level. It is intended, among other things, as a modeling language for specification and simulation. We can also use it for hardware synthesis if we restrict ourselves to a subset that can be automatically translated into hardware.
Numerical Solutions of Differential Equations on FPGA-Enhanced Computers
Conventionally, to speed up scientific or engineering (S&E) computation programs on general-purpose computers, one may elect to use faster CPUs, more memory, systems with more efficient (though complicated) architecture, better software compilers, or even coding with assembly languages. With the emergence of Field Programmable Gate Array (FPGA) based Reconfigurable Computing (RC) technology, numerical scientists and engineers now have another option using FPGA devices as core components to address their computational problems. The hardware-programmable, low-cost, but powerful “FPGA-enhanced computer” has now become an attractive approach for many S&E applications. A new computer architecture model for FPGA-enhanced computer systems and its detailed hardware implementation are proposed for accelerating the solutions of computationally demanding and data intensive numerical PDE problems. New FPGAoptimized algorithms/methods for rapid executions of representative numerical methods such as Finite Difference Methods (FDM) and Finite Element Methods (FEM) are designed, analyzed, and implemented on it. Linear wave equations based on seismic data processing applications are adopted as the targeting PDE problems to demonstrate the effectiveness of this new computer model. Their sustained computational performances are compared with pure software programs operating on commodity CPUbased general-purpose computers. Quantitative analysis is performed from a hierarchical set of aspects as customized/extraordinary computer arithmetic or function units, compact but flexible system architecture and memory hierarchy, and hardwareoptimized numerical algorithms or methods that may be inappropriate for conventional general-purpose computers. The preferable property of in-system hardware reconfigurability of the new system is emphasized aiming at effectively accelerating the execution of complex multi-stage numerical applications. Methodologies for accelerating the targeting PDE problems as well as other numerical PDE problems, such as heat equations and Laplace equations utilizing programmable hardware resources are concluded, which imply the broad usage of the proposed FPGA-enhanced computers.
Accelerating Gauss-Newton Filters on FPGAs
Radar tracking filters are generally computationally expensive, involving the manipulation of large matrices and deeply nested loops. In addition, they must generally work in real-time to be of any use. The now-common Kalman Filter was developed in the 1960's specifically for the purposes of lowering its computational burden, so that it could be implemented using the limited computational resources of the time. However, with the exponential increases in computing power since then, it is now possible to reconsider more heavy-weight, robust algorithms such as the original nonrecursive Gauss-Newton filter on which the Kalman filter is based[54]. This dissertation investigates the acceleration of such a filter using FPGA technology, making use of custom, reduced-precision number formats.
Embedded Design Handbook
The Embedded Design Handbook complements the primary documentation for the Altera® tools for embedded system development. It describes how to most effectively use the tools, and recommends design styles and practices for developing, debugging, and optimizing embedded systems using Altera-provided tools. The handbook introduces concepts to new users of Altera’s embedded solutions, and helps to increase the design efficiency of the experienced user.
Why You Should be Using Python/MyHDL as Your HDL
Hardware Description Languages (HDLs) revolutionized the digital hardware design landscape when they were introduced 30 years ago. The majority of the complex digital hardware (IC and FPGA) - that has irreversibly changed our lives - was enabled by HDLs-mainly Verilog and VHDL. Although the mainstay HDLs have had much success, they haven't fundamentally changed since their inception. The defacto HDLs, Verilog and VHDL, have evolved over time, but this is good and bad. These languages have new features but some newer language constructs don't fit well with existing constructs - not a clean design. MyHDL strives to be an HDL based on proven concepts that can be powerful yet elegantly expressed (i.e. clean design)
Why You Should be Using Python/MyHDL as Your HDL
Hardware Description Languages (HDLs) revolutionized the digital hardware design landscape when they were introduced 30 years ago. The majority of the complex digital hardware (IC and FPGA) - that has irreversibly changed our lives - was enabled by HDLs-mainly Verilog and VHDL. Although the mainstay HDLs have had much success, they haven't fundamentally changed since their inception. The defacto HDLs, Verilog and VHDL, have evolved over time, but this is good and bad. These languages have new features but some newer language constructs don't fit well with existing constructs - not a clean design. MyHDL strives to be an HDL based on proven concepts that can be powerful yet elegantly expressed (i.e. clean design)
Accelerating Gauss-Newton Filters on FPGAs
Radar tracking filters are generally computationally expensive, involving the manipulation of large matrices and deeply nested loops. In addition, they must generally work in real-time to be of any use. The now-common Kalman Filter was developed in the 1960's specifically for the purposes of lowering its computational burden, so that it could be implemented using the limited computational resources of the time. However, with the exponential increases in computing power since then, it is now possible to reconsider more heavy-weight, robust algorithms such as the original nonrecursive Gauss-Newton filter on which the Kalman filter is based[54]. This dissertation investigates the acceleration of such a filter using FPGA technology, making use of custom, reduced-precision number formats.
Embedded Design Handbook
The Embedded Design Handbook complements the primary documentation for the Altera® tools for embedded system development. It describes how to most effectively use the tools, and recommends design styles and practices for developing, debugging, and optimizing embedded systems using Altera-provided tools. The handbook introduces concepts to new users of Altera’s embedded solutions, and helps to increase the design efficiency of the experienced user.
VHDL Tutorial
The purpose of this tutorial is to describe the modeling language VHDL. VHDL includes facilities for describing logical structure and function of digital systems at a number of levels of abstraction, from system level down to the gate level. It is intended, among other things, as a modeling language for specification and simulation. We can also use it for hardware synthesis if we restrict ourselves to a subset that can be automatically translated into hardware.
Numerical Solutions of Differential Equations on FPGA-Enhanced Computers
Conventionally, to speed up scientific or engineering (S&E) computation programs on general-purpose computers, one may elect to use faster CPUs, more memory, systems with more efficient (though complicated) architecture, better software compilers, or even coding with assembly languages. With the emergence of Field Programmable Gate Array (FPGA) based Reconfigurable Computing (RC) technology, numerical scientists and engineers now have another option using FPGA devices as core components to address their computational problems. The hardware-programmable, low-cost, but powerful “FPGA-enhanced computer” has now become an attractive approach for many S&E applications. A new computer architecture model for FPGA-enhanced computer systems and its detailed hardware implementation are proposed for accelerating the solutions of computationally demanding and data intensive numerical PDE problems. New FPGAoptimized algorithms/methods for rapid executions of representative numerical methods such as Finite Difference Methods (FDM) and Finite Element Methods (FEM) are designed, analyzed, and implemented on it. Linear wave equations based on seismic data processing applications are adopted as the targeting PDE problems to demonstrate the effectiveness of this new computer model. Their sustained computational performances are compared with pure software programs operating on commodity CPUbased general-purpose computers. Quantitative analysis is performed from a hierarchical set of aspects as customized/extraordinary computer arithmetic or function units, compact but flexible system architecture and memory hierarchy, and hardwareoptimized numerical algorithms or methods that may be inappropriate for conventional general-purpose computers. The preferable property of in-system hardware reconfigurability of the new system is emphasized aiming at effectively accelerating the execution of complex multi-stage numerical applications. Methodologies for accelerating the targeting PDE problems as well as other numerical PDE problems, such as heat equations and Laplace equations utilizing programmable hardware resources are concluded, which imply the broad usage of the proposed FPGA-enhanced computers.
Why You Should be Using Python/MyHDL as Your HDL
Hardware Description Languages (HDLs) revolutionized the digital hardware design landscape when they were introduced 30 years ago. The majority of the complex digital hardware (IC and FPGA) - that has irreversibly changed our lives - was enabled by HDLs-mainly Verilog and VHDL. Although the mainstay HDLs have had much success, they haven't fundamentally changed since their inception. The defacto HDLs, Verilog and VHDL, have evolved over time, but this is good and bad. These languages have new features but some newer language constructs don't fit well with existing constructs - not a clean design. MyHDL strives to be an HDL based on proven concepts that can be powerful yet elegantly expressed (i.e. clean design)
Accelerating Gauss-Newton Filters on FPGAs
Radar tracking filters are generally computationally expensive, involving the manipulation of large matrices and deeply nested loops. In addition, they must generally work in real-time to be of any use. The now-common Kalman Filter was developed in the 1960's specifically for the purposes of lowering its computational burden, so that it could be implemented using the limited computational resources of the time. However, with the exponential increases in computing power since then, it is now possible to reconsider more heavy-weight, robust algorithms such as the original nonrecursive Gauss-Newton filter on which the Kalman filter is based[54]. This dissertation investigates the acceleration of such a filter using FPGA technology, making use of custom, reduced-precision number formats.
Embedded Design Handbook
The Embedded Design Handbook complements the primary documentation for the Altera® tools for embedded system development. It describes how to most effectively use the tools, and recommends design styles and practices for developing, debugging, and optimizing embedded systems using Altera-provided tools. The handbook introduces concepts to new users of Altera’s embedded solutions, and helps to increase the design efficiency of the experienced user.
VHDL Tutorial
The purpose of this tutorial is to describe the modeling language VHDL. VHDL includes facilities for describing logical structure and function of digital systems at a number of levels of abstraction, from system level down to the gate level. It is intended, among other things, as a modeling language for specification and simulation. We can also use it for hardware synthesis if we restrict ourselves to a subset that can be automatically translated into hardware.
Numerical Solutions of Differential Equations on FPGA-Enhanced Computers
Conventionally, to speed up scientific or engineering (S&E) computation programs on general-purpose computers, one may elect to use faster CPUs, more memory, systems with more efficient (though complicated) architecture, better software compilers, or even coding with assembly languages. With the emergence of Field Programmable Gate Array (FPGA) based Reconfigurable Computing (RC) technology, numerical scientists and engineers now have another option using FPGA devices as core components to address their computational problems. The hardware-programmable, low-cost, but powerful “FPGA-enhanced computer” has now become an attractive approach for many S&E applications. A new computer architecture model for FPGA-enhanced computer systems and its detailed hardware implementation are proposed for accelerating the solutions of computationally demanding and data intensive numerical PDE problems. New FPGAoptimized algorithms/methods for rapid executions of representative numerical methods such as Finite Difference Methods (FDM) and Finite Element Methods (FEM) are designed, analyzed, and implemented on it. Linear wave equations based on seismic data processing applications are adopted as the targeting PDE problems to demonstrate the effectiveness of this new computer model. Their sustained computational performances are compared with pure software programs operating on commodity CPUbased general-purpose computers. Quantitative analysis is performed from a hierarchical set of aspects as customized/extraordinary computer arithmetic or function units, compact but flexible system architecture and memory hierarchy, and hardwareoptimized numerical algorithms or methods that may be inappropriate for conventional general-purpose computers. The preferable property of in-system hardware reconfigurability of the new system is emphasized aiming at effectively accelerating the execution of complex multi-stage numerical applications. Methodologies for accelerating the targeting PDE problems as well as other numerical PDE problems, such as heat equations and Laplace equations utilizing programmable hardware resources are concluded, which imply the broad usage of the proposed FPGA-enhanced computers.
