A Golden Age of Hardware Description Languages: Applying Programming Language Techniques to Improve Design Productivity

Leading experts have declared that there is an impending golden age of computer architecture. During this age, the rate at which architects will be able to innovate will be directly tied to the design and implementation of the hardware description languages they use. Thus, the programming languages community stands on the critical path to this new golden age. This implies that we are also on the cusp of a golden age of hardware description languages. In this paper, we discuss the intellectual challenges facing researchers interested in hardware description language design, compilers, and formal methods. The major theme will be identifying opportunities to apply programming language techniques to address issues in hardware design productivity. Then, we present a vision for a multi-language system that provides a framework for developing solutions to these intellectual problems. This vision is based on a meta-programmed host language combined with a core embedded hardware description language that is used as the basis for the research and development of a sea of domain-specific languages. Central to the design of this system is the core language which is based on an abstraction that provides a general mechanism for the composition of hardware components described in any language

Application-Specific Memory Subsystems

The disparity in performance between processors and main memories has led computer architects to incorporate large cache hierarchies in modern computers. These cache hierarchies are designed to be general-purpose in that they strive to provide the best possible performance across a wide range of applications. However, such a memory subsystem does not necessarily provide the best possible performance for a particular application. Although general-purpose memory subsystems are desirable when the work-load is unknown and the memory subsystem must remain fixed, when this is not the case a custom memory subsystem may be beneficial. For example, in an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA) designed to run a particular application, a custom memory subsystem optimized for that application would be desirable. In addition, when there are tunable parameters in the memory subsystem, it may make sense to change these parameters depending on the application being run. Such a situation arises today with FPGAs and, to a lesser extent, GPUs, and it is plausible that general-purpose computers will begin to support greater flexibility in the memory subsystem in the future. In this dissertation, we first show that it is possible to create application-specific memory subsystems that provide much better performance than a general-purpose memory subsystem. In addition, we show a way to discover such memory subsystems automatically using a superoptimization technique on memory address traces gathered from applications. This allows one to generate a custom memory subsystem with little effort. We next show that our memory subsystem superoptimization technique can be used to optimize for objectives other than performance. As an example, we show that it is possible to reduce the number of writes to the main memory, which can be useful for main memories with limited write durability, such as flash or Phase-Change Memory (PCM). Finally, we show how to superoptimize memory subsystems for streaming applications, which are a class of parallel applications. In particular, we show that, through the use of ScalaPipe, we can author and deploy streaming applications targeting FPGAs with superoptimized memory subsystems. ScalaPipe is a domain-specific language (DSL) embedded in the Scala programming language for generating streaming applications that can be implemented on CPUs and FPGAs. Using the ScalaPipe implementation, we are able to demonstrate actual performance improvements using the superoptimized memory subsystem with applications implemented in hardware