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

STATISTICAL MACHINE LEARNING BASED MODELING FRAMEWORK FOR DESIGN SPACE EXPLORATION AND RUN-TIME CROSS-STACK ENERGY OPTIMIZATION FOR MANY-CORE PROCESSORS

The complexity of many-core processors continues to grow as a larger number of heterogeneous cores are integrated on a single chip. Such systems-on-chip contains computing structures ranging from complex out-of-order cores, simple in-order cores, digital signal processors (DSPs), graphic processing units (GPUs), application specific processors, hardware accelerators, I/O subsystems, network-on-chip interconnects, and large caches arranged in complex hierarchies. While the industry focus is on putting higher number of cores on a single chip, the key challenge is to optimally architect these many-core processors such that performance, energy and area constraints are satisfied. The traditional approach to processor design through extensive cycle accurate simulations are ill-suited for designing many-core processors due to the large microarchitecture design space that must be explored. Additionally it is hard to optimize such complex processors and the applications that run on them statically at design time such that performance and energy constraints are met under dynamically changing operating conditions. The dissertation establishes statistical machine learning based modeling framework that enables the efficient design and operation of many-core processors that meets performance, energy and area constraints. We apply the proposed framework to rapidly design the microarchitecture of a many-core processor for multimedia, computer graphics rendering, finance, and data mining applications derived from the Parsec benchmark. We further demonstrate the application of the framework in the joint run-time adaptation of both the application and microarchitecture such that energy availability constraints are met

Low-Power Wireless Distributed SIMD Architecture Concept: An 8051 Based Remote Execution Unit

Power has become a critical aspect in the design of modern wireless systems, especially in passive device nodes such as Radio Frequency Identification (RFID) tags, sensor nodes etc. Passive RFID tags in particular use simple logic that is used to respond with a unique code or data to identify objects when queried by an interrogator, whereas wireless passive sensor devices use microcontrollers for sensor data processing. There is a need for a Minimal Instruction Set Architecture (MISA) for such passive nodes with regard to low power. In this context, passive node capabilities need to be explored, possibly to suit target applications, in order to enable more than just identification and perhaps less than those of a conventional microcontroller Instruction Set Architecture (ISA). This dissertation research demonstrates a low-power wireless distributed processor architecture concept. The data and program instructions are stored on a powered interrogator providing wireless supervisory control for the remote passive node that has a basic processing core called the remote execution unit (REU). The interrogator and the passive node (REU) combination can be viewed as a complete processor or as multiple processing units forming the basis for a wireless distributed Single Instruction Multiple Data (SIMD) processor. This research introduces and investigates the REU architecture using an 8051-MISA with the goal of reducing power consumption of the system. A novel low power data-driven symbol decoder-CRC along with the 8051-MISA based execution core design form the frontend and core part of the REU architecture. Clocked and asynchronous digital logic implementations of the REU core design are presented and correspondingly the power, area and speed comparisons are also provided. Lack of strong support by commercial CAD tools is a major hurdle for synthesis of asynchronous designs. This research also presents a high-level design flow used to implement the asynchronous logic for the REU using traditional clocked CAD flows. This research work demonstrates immense potential to realize low power wireless passive sensor nodes for biomedical, automation, environmental, etc., applications especially while providing the basis for a programmable passive remote unit for distributed processing

ENERGY-EFFICIENT FEATURE EXTRACTION ENGINE AND SECURE CHIP IDENTIFICATION FOR UBIQUITOUS SURVEILLANCE

Ph.DDOCTOR OF PHILOSOPH

Exploring the Processor and ISA Design for Wireless Sensor Network Applications

Power consumption, physical size, and architecture design of sensor node processors have been the focus of sensor network research in the architecture community. What lies at the foundation for these research is the hardwarelevel design which determines the boundaries for achievable utility and performance. Architecture design and evaluation, however, cannot be accomplished independent of the applications and software that run on these sensor nodes. On one hand, some researchers have proposed architectures that can cater to a variety of application classes while trading off on some performance improvements. On the other hand, a set of application-specific architectures have been proposed which perform certain operations extremely well but are not versatile enough to run a variety of applications. This paper provides a design space exploration and optimizations platform to characterize the processor and ISA design tailored for a particular application or a class of applications. We collect a wide variety of sensor network applications to create a comprehensive benchmark suite called the WiSeNBench. We then present a careful profiling of these benchmark applications using an ARM simulator to identify some of the key characteristic behaviors. This also opens up avenue for a possible re-look at the classes of applications that could be supported on next-generation sensor networks and efficient architectural designs to enable these applications.

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts