Edwin Hsing-Mean Sha

Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems

In high-level synthesis for real-time embedded systems using heterogeneous functional units (FUs), it is critical to select the best FU type for each task. However, some tasks may not have fixed execution times. This article models each varied execution time as a probabilistic random variable and solves heterogeneous assignment with probability (HAP) problem. The solution of the HAP problem assigns a proper FU type to each task such that the total cost is minimized while the timing constraint is satisfied with a guaranteed confidence probability. The solutions to the HAP problem are useful for both hard real-time and soft real-time systems. Optimal algorithms are proposed to find the optimal solutions for the HAP problem when the input is a tree or a simple path. Two other algorithms, one is optimal and the other is near-optimal heuristic, are proposed to solve the general problem. The experiments show that our algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, our algorithms achieve an average reduction of 33.0% on total cost with 0.90 confidence probability satisfying timing constraints compared with the previous work using worst-case scenario.

A novel multiplexer-based low-power full adder

The 1-bit full adder circuit is a very important component in the design of application specific integrated circuits. This paper presents a novel low-power multiplexer-based 1-bit full adder that uses 12 transistors (MBA-12T). In addition to reduced transition activity and charge recycling capability, this circuit has no direct connections to the power-supply nodes, leading to a noticeable reduction in short-current power consumption. Intensive HSPICE simulation shows that the new adder has more than 26% in power savings over conventional 28-transistor CMOS adder and it consumes 23% less power than 10-transistor adders (SERF and 10T ) and is 64% faster.

Rotation Scheduling: A Loop Pipelining Algorithm

We consider the resource-constrained scheduling of loops with inter-iteration dependencies. A loop is modeled as a data flow graph (DFG), where edges are labeled with the number of iterations between dependencies. We design a novel and flexible technique, called rotation scheduling, for scheduling cyclic DFGs using loop pipelining. The rotation technique repeatedly transforms a schedule to a more compact schedule. We provide a theoretical basis for the operations based on retiming. We propose two heuristics to perform rotation scheduling, and give experimental results showing that they have very good performance.

Trust prediction and trust-based source routing in mobile ad hoc networks

Abstract Mobile ad hoc networks (MANETs) are spontaneously deployed over a geographically limited area without well-established infrastructure. The networks work well only if the mobile nodes are trusty and behave cooperatively. Due to the openness in network topology and absence of a centralized administration in management, MANETs are very vulnerable to various attacks from malicious nodes. In order to reduce the hazards from such nodes and enhance the security of network, this paper presents a dynamic trust prediction model to evaluate the trustworthiness of nodes, which is based on the nodes’ historical behaviors, as well as the future behaviors via extended fuzzy logic rules prediction. We have also integrated the proposed trust predication model into the Source Routing Mechanism. Our novel on-demand trust-based unicast routing protocol for MANETs, termed as Trust-based Source Routing protocol (TSR), provides a flexible and feasible approach to choose the shortest route that meets the security requirement of data packets transmission. Extensive experiments have been conducted to evaluate the efficiency and effectiveness of the proposed mechanism in malicious node identification and attack resistance. The results show that TSR improves packet delivery ratio and reduces average end-to-end latency.

Rotation scheduling: a loop pipelining algorithm

We consider the resource-constrained scheduling of loops with interiteration dependencies. A loop is modeled as a data flow graph (DFG), where edges are labeled with the number of iterations between dependencies. We design a novel and flexible technique, called rotation scheduling, for scheduling cyclic DFGs using loop pipelining. The rotation technique repeatedly transforms a schedule to a more compact schedule. We provide a theoretical basis for the operations based on retiming. We propose two heuristics to perform rotation scheduling and give experimental results showing that they have very good performance.

Reducing write activities on non-volatile memories in embedded CMPs via data migration and recomputation

Recent advances in circuit and process technologies have pushed non-volatile memory technologies into a new era. These technologies exhibit appealing properties such as low power consumption, non-volatility, shock-resistivity, and high density. However, there are challenges to which we need answers in the road of applying non-volatile memories as main memory in computer systems. First, non-volatile memories have limited number of write/erase cycles compared with DRAM memory. Second, write activities on non-volatile memory are more expensive than DRAM memory in terms of energy consumption and access latency. Both challenges will benefit from reduction of the write activities on the nonvolatile memory. In this paper, we target embedded Chip Multiprocessors (CMPs) with Scratch Pad Memory (SPM) and non-volatile main memory. We introduce data migration and recompu-tation techniques to reduce the number of write activities on non-volatile memories. Experimental results show that the proposed methods can reduce the number of writes by 59.41% on average, which means that the non-volatile memory can last 2.8 times as long as before. Meanwhile, the finish time of programs is reduced by 31.81% on average.

Towards energy efficient hybrid on-chip Scratch Pad Memory with non-volatile memory

Scratch Pad Memory (SPM), a software-controlled on-chip memory, has been widely adopted in many embedded systems due to its small area and low power consumption. As technology scaling reaches the sub-micron level, leakage energy consumption is surpassing dynamic energy consumption and becoming a critical issue. In this paper, we propose a novel hybrid SPM which consists of non-volatile memory (NVM) and SRAM to take advantage of the ultra-low leakage power consumption and high density of NVM as well as the efficient writes of SRAM. A novel dynamic data allocation algorithm is proposed to make use of the full potential of both NVM and SRAM. According to the experimental results, with the help of the proposed algorithm, the novel hybrid SPM architecture can reduce memory access time by 18.17%, dynamic energy by 24.29%, and leakage power by 37.34% on average compared with a pure SRAM based SPM with the same size area.

Static scheduling for synthesis of DSP algorithms on various models

Given a behavioral description of a DSP algorithm represented by a data-flow graph, we show how to obtain a rate-optimal static schedule with the minimum unfolding factor under two models, integral grid model and fractional grid model, and two kinds of implementations for each model, pipelined implementation and non-pipelined implementation. We present a simple and unified approach to deal with the four possible combinations. A unified polynomial-time scheduling algorithm is presented, which works on the original data-flow graphs without really unfolding. The values of the minimum rate-optimal unfolding factors and the general properties for all the four combinations are proved.

Efficient assignment and scheduling for heterogeneous DSP systems

This paper addresses high level synthesis for real-time digital signal processing (DSP) architectures using heterogeneous functional units (FUs). For such special purpose architecture synthesis, an important problem is how to assign a proper FU type to each operation of a DSP application and generate a schedule in such a way that all requirements can be met and the total cost can be minimized. We propose a two-phase approach to solve this problem. In the first phase, we solve the heterogeneous assignment problem, i.e., how to assign proper FU types to applications such that the total cost can be minimized while the timing constraint is satisfied. In the second phase, based on the assignments obtained in the first phase, we propose a minimum resource scheduling algorithm to generate a schedule and a feasible configuration that uses as little resource as possible. We prove that the heterogeneous assignment problem is NP-complete. Efficient algorithms are proposed to find an optimal solution when the given DFG is a simple path or a tree. Three other algorithms are proposed to solve the general problem. The experiments show that our algorithms can effectively reduce the total cost compared with the previous work.

Overhead-aware energy optimization for real-time streaming applications on multiprocessor System-on-Chip

In this article, we focus on solving the energy optimization problem for real-time streaming applications on multiprocessor System-on-Chip by combining task-level coarse-grained software pipelining with DVS (Dynamic Voltage Scaling) and DPM (Dynamic Power Management) considering transition overhead, inter-core communication and discrete voltage levels. We propose a two-phase approach to solve the problem. In the first phase, we propose a coarse-grained task parallelization algorithm called RDAG to transform a periodic dependent task graph into a set of independent tasks by exploiting the periodic feature of streaming applications. In the second phase, we propose a scheduling algorithm, GeneS, to optimize energy consumption. GeneS is a genetic algorithm that can search and find the best schedule within the solution space generated by gene evolution. We conduct experiments with a set of benchmarks from E3S and TGFF. The experimental results show that our approach can achieve a 24.4% reduction in energy consumption on average compared with the previous work.