Making Wide-Issue VLIW Processors Viable on FPGAs

Soft and highly-customized processors are emerging as a common way to efficiently control large amount of computing resources available on FPGAs. Yet, some processor architectures of choice for DSP and media applications, such as wide-issue VLIW processors, remain impractical: the multi-ported register file makes a very inefficient use of the resources in the FPGA fabric. This paper proposes modifications to existing FPGAs to make soft-VLIW processor viable. We introduce an embedded multi-ported RAM that can be customized to match the issue-width of VLIW processors. To ascertain the benefits of this approach, we map an extensible VLIW processor onto a standard FPGA from Xilinx. For the register file implemented in the modified FPGA, the area occupied is at least 102x smaller and the dynamic power is reduced by 41% as compared to the implementation using configurable logic blocks in existing standard FPGAs. A subset of this embedded multi-ported RAM can also be used for mapping the register file in soft-RISC processors. For the soft-RISC processor, the register file in the modified FPGA is at least 22x smaller than its equivalent that uses configurable logic blocks and 1.5 x the size in comparison to the implementation using block RAMs. Reduction of routing area and the maximum net length is about 39% and 51% respectively for RISC processors. As a result, this approach works towards enhancing soft-processor density in FPGAs by orders of magnitude.

FPGAs for the Masses: Affordable Hardware Synthesis from Domain-Specific Languages

Nithin George

Field Programmable Gate Arrays (FPGAs) have the unique ability to be configured into application-specific architectures that are well suited to specific computing problems. This enables them to achieve performances and energy efficiencies that outclass other processor-based architectures, such as Chip Multiprocessors (CMPs), Graphic Processing Units (GPUs) and Digital Signal Processors (DSPs). Despite this, FPGAs are yet to gain widespread adoption, especially among application and software developers, because of their laborious application development process that requires hardware design expertise. In some application areas, domain-specific hardware synthesis tools alleviate this problem by using a Domain-Specific Language (DSL) to hide the low-level hardware details and also improve productivity of the developer. Additionally, these tools leverage domain knowledge to perform optimizations and produce high-quality hardware designs. While this approach holds great promise, the significant effort and cost of developing such domain-specific tools make it unaffordable in many application areas. In this thesis, we develop techniques to reduce the effort and cost of developing domain-specific hardware synthesis tools. To demonstrate our approach, we develop a toolchain to generate complete hardware systems from high-level functional specifications written in a DSL. Firstly, our approach uses language embedding and type-directed staging to develop a DSL and compiler in a cost-effective manner. To further reduce effort, we develop this compiler by composing reusable optimization modules, and integrate it with existing hardware synthesis tools. However, most synthesis tools require users to have hardware design knowledge to produce high-quality results. Therefore, secondly, to facilitate people without hardware design skills to develop domain-specific tools, we develop a methodology to generate high-quality hardware designs from well known computational patterns, such as map, zipWith, reduce and foreach; computational patterns are algorithmic methods that capture the nature of computation and communication and can be easily understood and used without expert knowledge. In our approach, we decompose the DSL specifications into constituent computational patterns and exploit the properties of these patterns, such as degree of parallelism, interdependence between operations and data-access characteristics, to generate high-quality hardware modules to implement them, and compose them into a complete system design. Lastly, we extended our methodology to automatically parallelize computations across multiple hardware modules to benefit from the spatial parallelism of the FPGA as well as overcome performance problems caused by non-sequential data access patterns and long access latency to external memory. To achieve this, we utilize the data-access properties of the computational patterns to automatically identify synchronization requirements and generate such multi-module designs from the same high-level functional specifications. Driven by power and performance constraints, today the world is turning to reconfigurable technology (i.e., FPGAs) to meet the computational needs of tomorrow. In this light, this work addresses the cardinal problem of making tomorrow's computing infrastructure programmable to application developers.

EPFL2016

LTRF: Enabling High-Capacity Register Files for GPUs via Hardware/Software Cooperative Register Prefetching

Mario Paulo Drumond Lages De Oliveira, Seyed Borna Ehsani, Babak Falsafi, Hamid Sarbazi-Azad

Graphics Processing Units (GPUs) employ large register files to accommodate all active threads and accelerate context switching. Unfortunately, register files are a scalability bottleneck for future GPUs due to long access latency, high power consumption, and large silicon area provisioning. Prior work proposes hierarchical register file, to reduce the register file power consumption by caching registers in a smaller register file cache. Unfortunately, this approach does not improve register access latency due to the low hit rate in the register file cache. In this paper, we propose the Latency-Tolerant Register File (LTRF) architecture to achieve low latency in a two-level hierarchical structure while keeping power consumption low. We observe that compile-time interval analysis enables us to divide GPU program execution into intervals with an accurate estimate of a warp’s aggregate register working-set within each interval. The key idea of LTRF is to prefetch the estimated register working-set from the main register file to the register file cache under software control, at the beginning of each interval, and overlap the prefetch latency with the execution of other warps. Our experimental results show that LTRF enables high-capacity yet long-latency main GPU register files, paving the way for various optimizations. As an example optimization, we implement the main register file with emerging high-density high-latency memory technologies, enabling 8× larger capacity and improving overall GPU performance by 31% while reducing register file power consumption by 46%.

2018

FPGAs for the Masses: Affordable Hardware Synthesis from Domain-Specific Languages

Nithin George

EPFL2016