Simultaneous Peak and Average Power Minimization During Datapath Scheduling

In low power design for deep submicron and nanometer regimes, the peak power, power fluctuation, average power and total energy are equally design constraints. In this work, we propose datapath scheduling algorithms for simultaneous minimization of peak and average power. The minimization schemes based on integer linear programming (ILP) are developed for the design of datapaths that can function in three modes of operation: (1) single supply voltage and single frequency (SVSF), (2) multiple supply voltages and dynamic frequency clocking (MVDFC) and (3) multiple supply voltages and multicycling (MVMC). The techniques are evaluated by estimating the peak power consumption, the average power consumption and the power delay product of selected high level synthesis benchmark circuits for different resource constraints. Experimental results indicate that combining multiple supply voltages and dynamic frequency clocking, yields significant reductions in the peak power, the average power, and the power delay product

Application-Specific Things Architectures for IoT-Based Smart Healthcare Solutions

Human body is a complex system organized at different levels such as cells, tissues and organs, which contributes to 11 important organ systems. The functional efficiency of this complex system is evaluated as health. Traditional healthcare is unable to accommodate everyone's need due to the ever-increasing population and medical costs. With advancements in technology and medical research, traditional healthcare applications are shaping into smart healthcare solutions. Smart healthcare helps in continuously monitoring our body parameters, which helps in keeping people health-aware. It provides the ability for remote assistance, which helps in utilizing the available resources to maximum potential. The backbone of smart healthcare solutions is Internet of Things (IoT) which increases the computing capacity of the real-world components by using cloud-based solutions. The basic elements of these IoT based smart healthcare solutions are called "things." Things are simple sensors or actuators, which have the capacity to wirelessly connect with each other and to the internet. The research for this dissertation aims in developing architectures for these things, focusing on IoT-based smart healthcare solutions. The core for this dissertation is to contribute to the research in smart healthcare by identifying applications which can be monitored remotely. For this, application-specific thing architectures were proposed based on monitoring a specific body parameter; monitoring physical health for family and friends; and optimizing the power budget of IoT body sensor network using human body communications. The experimental results show promising scope towards improving the quality of life, through needle-less and cost-effective smart healthcare solutions

Power and memory optimization techniques in embedded systems design

Embedded systems incur tight constraints on power consumption and memory (which impacts size) in addition to other constraints such as weight and cost. This dissertation addresses two key factors in embedded system design, namely minimization of power consumption and memory requirement. The first part of this dissertation considers the problem of optimizing power consumption (peak power as well as average power) in high-level synthesis (HLS). The second part deals with memory usage optimization mainly targeting a restricted class of computations expressed as loops accessing large data arrays that arises in scientific computing such as the coupled cluster and configuration interaction methods in quantum chemistry. First, a mixed-integer linear programming (MILP) formulation is presented for the scheduling problem in HLS using multiple supply-voltages in order to optimize peak power as well as average power and energy consumptions. For large designs, the MILP formulation may not be suitable; therefore, a two-phase iterative linear programming formulation and a power-resource-saving heuristic are presented to solve this problem. In addition, a new heuristic that uses an adaptation of the well-known force-directed scheduling heuristic is presented for the same problem. Next, this work considers the problem of module selection simultaneously with scheduling for minimizing peak and average power consumption. Then, the problem of power consumption (peak and average) in synchronous sequential designs is addressed. A solution integrating basic retiming and multiple-voltage scheduling (MVS) is proposed and evaluated. A two-stage algorithm namely power-oriented retiming followed by a MVS technique for peak and/or average power optimization is presented. Memory optimization is addressed next. Dynamic memory usage optimization during the evaluation of a special class of interdependent large data arrays is considered. Finally, this dissertation develops a novel integer-linear programming (ILP) formulation for static memory optimization using the well-known fusion technique by encoding of legality rules for loop fusion of a special class of loops using logical constraints over binary decision variables and a highly effective approximation of memory usage

Simultaneous Peak and Average Power Minimization during Datapath Scheduling for DSP Processors

The use of multiple supply voltages for energy and average power reduction is well researched and several works have appeared in the literature. However, in low power design using deep submicron and nanometer technology, the peak power, peak power differential, average power and total energy are equally critical design constraints. In this work, we propose datapath scheduling algorithms for simultaneous minimization of peak and average power while maintaining performance by use of dynamic frequency clocking and multiple supply voltages. The algorithms use integer linear programming based models. The dynamic frequency clocking methodology is more useful for data intensive signal processing applications. The effectiveness of our scheduling technique is measured by estimating the peak power consumption, the average power consumption and the power delay product of the datapath circuit. Furthermore, the proposed scheduling scheme is compared with combined multiple supply voltages and multicycling scheme. Experimental results show that combined multiple supply voltages ( 0 ) and dynamic frequency clocking scheme achieves significant reductions in peak power ( on the average), average power ( on the average) and power delay product ( on the average). Categories and Subject Descriptors B.5.1 [Register-Transfer-Level Implementation]: Datapath Design; B.5.2 [Register-Transfer-Level Implementation]: Automatic Synthesis, Optimization; G.1.6 [Numerical Analysis]: Optimization, Integer Programming General Terms Algorithms, Performance, Design, Reliability Keywords Peak power, Average Power, High-level Synthesis, Datapath Scheduling, Multiple Voltages, Dynamic Frequency Clocking Permission to make digital or hard copies of all or part of this work for ..

Interconnect-aware scheduling and resource allocation for high-level synthesis

A high-level architectural synthesis can be described as the process of transforming a behavioral description into a structural description. The scheduling, processor allocation, and register binding are the most important tasks in the high-level synthesis. In the past, it has been possible to focus simply on the delays of the processing units in a high-level synthesis and neglect the wire delays, since the overall delay of a digital system was dominated by the delay of the logic gates. However, with the process technology being scaled down to deep-submicron region, the global interconnect delays can no longer be neglected in VLSI designs. It is, therefore, imperative to include in high-level synthesis the delays on wires and buses used to communicate data between the processing units i.e., inter-processor communication delays. Furthermore, the way the process of register binding is performed also has an impact on the complexity of the interconnect paths required to transfer data between the processing units. Hence, the register binding can no longer ignore its effect on the wiring complexity of resulting designs. The objective of this thesis is to develop techniques for an interconnect-aware high-level synthesis. Under this common theme, this thesis has two distinct focuses. The first focus of this thesis is on developing a new high-level synthesis framework while taking the inter-processor communication delay into consideration. The second focus of this thesis is on the developing of a technique to carry out the register binding and a scheme to reduce the number of registers while taking the complexity of the interconnects into consideration. A novel scheduling and processor allocation technique taking into consideration the inter-processor communication delay is presented. In the proposed technique, the communication delay between a pair of nodes of different types is treated as a non-computing node, whereas that between a pair of nodes of the same type is taken into account by re-adjusting the firing times of the appropriate nodes of the data flow graph (DFG). Another technique for the integration of the placement process into the scheduling and processor allocation in order to determine the actual positions of the processing units in the placement space is developed. The proposed technique makes use of a hybrid library of functional units, which includes both operation-specific and reconfigurable multiple-operation functional units, to maximize the local data transfer. A technique for register binding that results in a reduced number of registers and interconnects is developed by appropriately dividing the lifetime of a token into multiple segments and then binding those having the same source and/or destination into a single register. A node regeneration scheme, in which the idle processing units are utilized to generate multiple copies of the nodes in a given DFG, is devised to reduce the number of registers and interconnects even further. The techniques and schemes developed in this thesis are applied to the synthesis of architectures for a number of benchmark DSP problems and compared with various other commonly used synthesis methods in order to assess their effectiveness. It is shown that the proposed techniques provide superior performance in terms of the iteration period, placement area, and the numbers of the processing units, registers and interconnects in the synthesized architectur

High-level synthesis of dataflow programs for heterogeneous platforms:design flow tools and design space exploration

The growing complexity of digital signal processing applications implemented in programmable logic and embedded processors make a compelling case the use of high-level methodologies for their design and implementation. Past research has shown that for complex systems, raising the level of abstraction does not necessarily come at a cost in terms of performance or resource requirements. As a matter of fact, high-level synthesis tools supporting such a high abstraction often rival and on occasion improve low-level design. In spite of these successes, high-level synthesis still relies on programs being written with the target and often the synthesis process, in mind. In other words, imperative languages such as C or C++, most used languages for high-level synthesis, are either modified or a constrained subset is used to make parallelism explicit. In addition, a proper behavioral description that permits the unification for hardware and software design is still an elusive goal for heterogeneous platforms. A promising behavioral description capable of expressing both sequential and parallel application is RVC-CAL. RVC-CAL is a dataflow programming language that permits design abstraction, modularity, and portability. The objective of this thesis is to provide a high-level synthesis solution for RVC-CAL dataflow programs and provide an RVC-CAL design flow for heterogeneous platforms. The main contributions of this thesis are: a high-level synthesis infrastructure that supports the full specification of RVC-CAL, an action selection strategy for supporting parallel read and writes of list of tokens in hardware synthesis, a dynamic fine-grain profiling for synthesized dataflow programs, an iterative design space exploration framework that permits the performance estimation, analysis, and optimization of heterogeneous platforms, and finally a clock gating strategy that reduces the dynamic power consumption. Experimental results on all stages of the provided design flow, demonstrate the capabilities of the tools for high-level synthesis, software hardware Co-Design, design space exploration, and power optimization for reconfigurable hardware. Consequently, this work proves the viability of complex systems design and implementation using dataflow programming, not only for system-level simulation but real heterogeneous implementations