Low-power Programmable Processor for Fast Fourier Transform Based on Transport Triggered Architecture

This paper describes a low-power processor tailored for fast Fourier transform computations where transport triggering template is exploited. The processor is software-programmable while retaining an energy-efficiency comparable to existing fixed-function implementations. The power savings are achieved by compressing the computation kernel into one instruction word. The word is stored in an instruction loop buffer, which is more power-efficient than regular instruction memory storage. The processor supports all power-of-two FFT sizes from 64 to 16384 and given 1 mJ of energy, it can compute 20916 transforms of size 1024.Comment: 5 pages, 4 figures, 1 table, ICASSP 2019 conferenc

Coarse-grained reconfigurable array architectures

Coarse-Grained Reconfigurable Array (CGRA) architectures accelerate the same inner loops that benefit from the high ILP support in VLIW architectures. By executing non-loop code on other cores, however, CGRAs can focus on such loops to execute them more efficiently. This chapter discusses the basic principles of CGRAs, and the wide range of design options available to a CGRA designer, covering a large number of existing CGRA designs. The impact of different options on flexibility, performance, and power-efficiency is discussed, as well as the need for compiler support. The ADRES CGRA design template is studied in more detail as a use case to illustrate the need for design space exploration, for compiler support and for the manual fine-tuning of source code

Design of testbed and emulation tools

The research summarized was concerned with the design of testbed and emulation tools suitable to assist in projecting, with reasonable accuracy, the expected performance of highly concurrent computing systems on large, complete applications. Such testbed and emulation tools are intended for the eventual use of those exploring new concurrent system architectures and organizations, either as users or as designers of such systems. While a range of alternatives was considered, a software based set of hierarchical tools was chosen to provide maximum flexibility, to ease in moving to new computers as technology improves and to take advantage of the inherent reliability and availability of commercially available computing systems

Mechanistic analytical modeling of superscalar in-order processor performance

Superscalar in-order processors form an interesting alternative to out-of-order processors because of their energy efficiency and lower design complexity. However, despite the reduced design complexity, it is nontrivial to get performance estimates or insight in the application--microarchitecture interaction without running slow, detailed cycle-level simulations, because performance highly depends on the order of instructions within the application’s dynamic instruction stream, as in-order processors stall on interinstruction dependences and functional unit contention. To limit the number of detailed cycle-level simulations needed during design space exploration, we propose a mechanistic analytical performance model that is built from understanding the internal mechanisms of the processor. The mechanistic performance model for superscalar in-order processors is shown to be accurate with an average performance prediction error of 3.2% compared to detailed cycle-accurate simulation using gem5. We also validate the model against hardware, using the ARM Cortex-A8 processor and show that it is accurate within 10% on average. We further demonstrate the usefulness of the model through three case studies: (1) design space exploration, identifying the optimum number of functional units for achieving a given performance target; (2) program--machine interactions, providing insight into microarchitecture bottlenecks; and (3) compiler--architecture interactions, visualizing the impact of compiler optimizations on performance

SAMIE-LSQ: set-associative multiple-instruction entry load/store queue

The load/store queue (LSQ) is one of the most complex parts of contemporary processors. Its latency is critical for the processor performance and it is usually one of the processor hotspots. This paper presents a highly banked, set-associative, multiple-instruction entry LSQ (SAMIE-LSQ,) that achieves high performance with small energy requirements. The SAMIE-LSQ classifies the memory instructions (loads and stores) based on the address to be accessed, and groups those instructions accessing the same cache line in the same entry. Our approach relies on the fact that many in-flight memory instructions access the same cache lines. Each SAMIE-LSQ entry has space for several memory instructions accessing the same cache line. This arrangement has a number of advantages. First, it significantly reduces the address comparison activity needed for memory disambiguation since there are less addresses to be compared. It also reduces the activity in the data TLB, the cache tag and cache data arrays. This is achieved by caching the cache line location and address translation in the corresponding SAMIE-LSQ entry once the access of one of the instructions in an entry is performed, so instructions that share an entry can reuse the translation, avoid the tag check and get the data directly from the concrete cache way without checking the others. Besides, the delay of the proposed scheme is lower than that required by a conventional LSQ. We show that the SAMIE-LSQ saves 82% dynamic energy for the load/store queue, 42% for the LI data cache and 73% for the data TLB, with a negligible impact on performance (0.6%)Peer ReviewedPostprint (published version

A proposed synthesis method for Application-Specific Instruction Set Processors

Due to the rapid technology advancement in integrated circuit era, the need for the high computation performance together with increasing complexity and manufacturing costs has raised the demand for high-performance con fi gurable designs; therefore, the Application-Speci fi c Instruction Set Processors (ASIPs) are widely used in SoC design. The automated generation of software tools for ASIPs is a commonly used technique, but the automated hardware model generation is less frequently applied in terms of fi nal RTL implementations. Contrary to this, the fi nal register-transfer level models are usually created, at least partly, manually. This paper presents a novel approach for automated hardware model generation for ASIPs. The new solution is based on a novel abstract ASIP model and a modeling language (Algorithmic Microarchitecture Description Language, AMDL) optimized for this architecture model. The proposed AMDL-based pre-synthesis method is based on a set of pre-de fi ned VHDL implementation schemes, which ensure the qualities of the automatically generated register-transfer level models in terms of resource requirement and operation frequency. The design framework implementing the algorithms required by the synthesis method is also presented

An efficient design space exploration framework to optimize power-efficient heterogeneous many-core multi-threading embedded processor architectures

By the middle of this decade, uniprocessor architecture performance had hit a roadblock due to a combination of factors, such as excessive power dissipation due to high operating frequencies, growing memory access latencies, diminishing returns on deeper instruction pipelines, and a saturation of available instruction level parallelism in applications. An attractive and viable alternative embraced by all the processor vendors was multi-core architectures where throughput is improved by using micro-architectural features such as multiple processor cores, interconnects and low latency shared caches integrated on a single chip. The individual cores are often simpler than uniprocessor counterparts, use hardware multi-threading to exploit thread-level parallelism and latency hiding and typically achieve better performance-power figures. The overwhelming success of the multi-core microprocessors in both high performance and embedded computing platforms motivated chip architects to dramatically scale the multi-core processors to many-cores which will include hundreds of cores on-chip to further improve throughput. With such complex large scale architectures however, several key design issues need to be addressed. First, a wide range of micro- architectural parameters such as L1 caches, load/store queues, shared cache structures and interconnection topologies and non-linear interactions between them define a vast non-linear multi-variate micro-architectural design space of many-core processors; the traditional method of using extensive in-loop simulation to explore the design space is simply not practical. Second, to accurately evaluate the performance (measured in terms of cycles per instruction (CPI)) of a candidate design, the contention at the shared cache must be accounted in addition to cycle-by-cycle behavior of the large number of cores which superlinearly increases the number of simulation cycles per iteration of the design exploration. Third, single thread performance does not scale linearly with number of hardware threads per core and number of cores due to memory wall effect. This means that at every step of the design process designers must ensure that single thread performance is not unacceptably slowed down while increasing overall throughput. While all these factors affect design decisions in both high performance and embedded many-core processors, the design of embedded processors required for complex embedded applications such as networking, smart power grids, battlefield decision-making, consumer electronics and biomedical devices to name a few, is fundamentally different from its high performance counterpart because of the need to consider (i) low power and (ii) real-time operations. This implies the design objective for embedded many-core processors cannot be to simply maximize performance, but improve it in such a way that overall power dissipation is minimized and all real-time constraints are met. This necessitates additional power estimation models right at the design stage to accurately measure the cost and reliability of all the candidate designs during the exploration phase. In this dissertation, a statistical machine learning (SML) based design exploration framework is presented which employs an execution-driven cycle- accurate simulator to accurately measure power and performance of embedded many-core processors. The embedded many-core processor domain is Network Processors (NePs) used to processed network IP packets. Future generation NePs required to operate at terabits per second network speeds captures all the aspects of a complex embedded application consisting of shared data structures, large volume of compute-intensive and data-intensive real-time bound tasks and a high level of task (packet) level parallelism. Statistical machine learning (SML) is used to efficiently model performance and power of candidate designs in terms of wide ranges of micro-architectural parameters. The method inherently minimizes number of in-loop simulations in the exploration framework and also efficiently captures the non-linear interactions between the micro-architectural design parameters. To ensure scalability, the design space is partitioned into (i) core-level micro-architectural parameters to optimize single core architectures subject to the real-time constraints and (ii) shared memory level micro- architectural parameters to explore the shared interconnection network and shared cache memory architectures and achieves overall optimality. The cost function of our exploration algorithm is the total power dissipation which is minimized, subject to the constraints of real-time throughput (as determined from the terabit optical network router line-speed) required in IP packet processing embedded application