A Novel Criticality Computation Method in Statistical Timing Analysis
The impact of process variations increases as technology scales to nanometer region. Under large process variations, the path and arc/node criticality [18] provide effective metrics in guiding circuit optimization. To facilitate the criticality computation considering the correlation, we define the critical region for the path and arc/node in a timing graph, and propose an efficient method to compute the criticality for paths and arcs/nodes simultaneously by a single breadth-first graph traversal during the backward propagation. Instead of choosing a set of paths for analysis prematurely, we develop a new property of the path criticality to prune those paths with low criticality at very earlier stages, so that our path criticality computation method has linear complexity with respect of the timing edges in a timing graph. To improve the computation accuracy, cutset and path criticality properties are exploited to calibrate the computation results. The experimental results on ISCAS benchmark circuits show that our criticality computation method can achieve high accuracy with fast speed.

title: Embedded Multi-Processor System-on-chip (MPSoC) design considering process variations
abstract: As technology scales, the delay uncertainty caused by process variations has become increasingly pronounced in deep sub-micron designs. As a result, a paradigm shift from deterministic to statistical design methodology at all levels of the design hierarchy is inevitable (Borkar, 2005). As part of two NSF projects (NSF CNS CAREER 0643902 and NSF CNS 0720659), a variation-aware task allocation and scheduling method for multiprocessor system-on-chip (MPSoC) architectures is proposed, to mitigate the impact of parameter variations. A new design metric, called performance yield and defined as the probability of the assigned schedule meeting the predefined performance constraints, is used to guide the task allocation and scheduling procedure. An efficient yield computation method for task scheduling complements and significantly improves the effectiveness of the proposed variation-aware scheduling algorithm. Experimental results show that our variation-aware scheduler achieves significant yield improvements.

title: Soft Error Rate Analysis for Combinational Logic Using an Accurate Electrical Masking Model
abstract: Accurate electrical masking modeling represents a significant challenge in soft error rate analysis for combinational logic circuits. In this paper, we use table lookup MOSFET models to accurately capture the nonlinear properties of submicron MOS transistors. Based on these models, we propose and validate the transient pulse generation model and propagation model for soft error rate analysis. The pulse generated by our pulse generation model matches well with that of HSPICE simulation, and the pulse propagation model provides nearly one order of magnitude improvement in accuracy over the previous models. Using these two models, we propose an accurate and efficient block-based soft error rate analysis method for combinational logic circuits.

title: Optimal topology exploration for application-specific 3D architectures
abstract: As technology scales, increasing interconnect costs make it necessary to consider alternate ways of building integrated circuits. One promising option along this direction is 3D architectures where a stack of multiple device layers, with direct vertical tunneling through them, are put together on the same chip. In this paper, we explore how processor cores and storage blocks can be placed in a 3D architecture to minimize data access costs under temperature constraints. This process is referred to as the topology exploration. Using integer linear programming, we compare the best 2D placement with the best 3D placement, and show through experiments with both single-core and multicore systems that the 3D placement generates much better results (in terms of data access costs) under the same temperature bounds. We also discuss the tradeoffs between temperature constraint and data access costs

title: Variability-driven module selection with joint design time optimization and post-silicon tuning
abstract: Increasing delay and power variation are significant challenges to the designers as technology scales to the deep sub-micron (DSM) regime. Traditional module selection techniques in high level synthesis use worst case delay/power information to perform the optimization, and therefore may be too pessimistic such that extra resources are used to guarantee design requirements. Parametric yield, which is defined as the probability of the synthesized hardware meeting the performance/power constraints, can be used to guide design space exploration. The parametric yield can be effectively improved by combining both design-time variation-aware optimization and post silicon tuning techniques (such as adaptive body biasing (ABB)). In this paper, we propose a module selection algorithm that combines design-time optimization with post- silicon tuning (using ABB) to maximize design yield. A variation-aware module selection algorithm based on efficient performance and power yield gradient computation is developed. The post silicon optimization is formulated as an efficient sequential conic program to determine the optimal body bias distribution, which in turn affects design-time module selection. The experiment results show that significant yield can be achieved compared to traditional worst-case driven module selection technique. To the best of our knowledge, this is the first variability-driven high level synthesis technique that considers post-silicon tuning during design time optimization.

title: Dependability analysis of nano-scale FinFET circuits
abstract: FinFET technology has been proposed as a promising alternative for deep sub-micro bulk CMOS technology, because of its better scalability. Previous works have studied the performance or power advantages of FinFET circuits over bulk CMOS circuits. This paper provides the dependability analysis of FinFET circuits, studying the soft error vulnerability of FinFET circuits and the impact of process variation. Our experiments compare FinFET circuits against bulk CMOS circuits in both 32 nm and 45 nm technologies, showing that FinFET circuits have better dependability and scalability, which is indicated by better soft error immunity and less impact of process variation. It is concluded that FinFET-based circuit design is more robust than the bulk CMOS based circuit design

title: OBFS: A File System for Object-Based Storage Devices
abstract: The object-based storage model, in which files are made up of o ne or more data objects stored on self-contained Object-Based Storage Devices (OSDs), is em erging as an architecture for distributed storage systems. The workload presented to the OSDs will be q uite different from that of generalpurpose file systems, yet many distributed file systems employ general-purpose file systems as their underlying file system. We present OBFS, a small and highly ef ficient file system designed for use in OSDs. Our experiments show that our user-level implement ation of OBFS outperforms Linux Ext2 and Ext3 by a factor of two or three, and while OBFS is 1/25 the size of XFS, it provides only slightly lower read performance and 10%‐40% higher write pe rformance.

title: Greedy Approximation Algorithms
abstract: Greedy strategy is a simple and natural method in the design of approximation algorithms. This chapter presents greedy approximation algorithms for very broad classes of maximization problems and minimization problems and analyzes their approximation bounds. A number of applications of these greedy approximation algorithms are also described in this chapter. For many applications, the greedy approximation algorithm can achieve the best known approximation bound in a straightforward manner. For some applications, they have different formulations, and the corresponding greedy approximation algorithms have different approximation bounds. In addition, some applications can have better approximation algorithm than the greedy approximation algorithm.

title: Using MEMS-based storage in computer systems---MEMS storage architectures
abstract: As an emerging nonvolatile secondary storage technology, MEMS-based storage exhibits several desirable properties including high performance, high storage volumic density, low power consumption, low entry cost, and small form factor. However, MEMS-based storage provides a limited amount of storage per device and is likely to be more expensive than magnetic disk. Systems designers will therefore need to make trade-offs to achieve well-balanced designs. We present an architecture in which MEMS devices are organized into MEMS storage enclosures with online spares. Such enclosures are proven to be highly reliable storage building bricks with no maintenance during their economic lifetimes. We also demonstrate the effectiveness of using MEMS as another layer in the storage hierarchy, bridging the cost and performance gap between MEMS storage and disk. We show that using MEMS as a disk cache can significantly improve system performance and cost-performance ratio.

title: Fault tolerant topology control for one-to-all communications in symmetric wireless networks
abstract: This paper introduces the problem of fault tolerant topology control for one-to-all communications in symmetric wireless networks. We investigate two algorithms to address the problem, namely Minimum Weight-Based Algorithm (MWBA) and Nearest Neighbour Augmentation Algorithm (NNAA) and prove that the former is a 4k approximation and the latter is a (k + 4) approximation. Through simulations, we evaluate the average performance of these two algorithms and find that MWBA is slightly better than NNAA in terms of the total power consumption.

title: Connected Dominating Sets in Wireless Networks with Different Transmission Ranges
abstract: Since there is no fixed infrastructure or centralized management in wireless ad hoc networks, a Connected Dominating Set (CDS) has been proposed to serve as a virtual backbone. The CDS of a graph representing a network has a significant impact on the efficient design of routing protocols in wireless networks. This problem has been studied extensively in Unit Disk Graphs (UDG), in which all nodes have the same transmission ranges. However, in practice, the transmission ranges of all nodes are not necessarily equal. In this paper, we model a network as a disk graph and introduce the CDS problem in disk graphs. We present two efficient approximation algorithms to obtain a minimum CDS. The performance ratio of these algorithms is constant if the ratio of the maximum transmission range over the minimum transmission range in the network is bounded. These algorithms can be implemented as distributed algorithms. Furthermore, we show a size relationship between a maximal independent set and a CDS as well as a bound of the maximum number of independent neighbors of a node in disk graphs. The theoretical analysis and simulation results are also presented to verify our approaches.