This study proposes a variable selection linear regression (VSLR) adaptation framework to improve the accuracy of automatic speech recognition (ASR) with only limited and unlabeled adaptation data. The proposed framework can be divided into three phases. The first phase prepares multiple variable subsets by applying a ranking filter to the original regression variable set. The second phase determines the best variable subset based on a pre-determined performance evaluation criterion and computes a linear regression (LR) mapping function based on the determined subset. The third phase performs adaptation in either model or feature spaces. The three phases can select the optimal components and remove redundancies in the LR mapping function effectively and thus enable VSLR to provide satisfactory adaptation performance even with a very limited number of adaptation statistics. We formulate model space VSLR and feature space VSLR by integrating the VS techniques into the conventional LR adaptation systems. Experimental results on the Aurora-4 task show that model space VSLR and feature space VSLR, respectively, outperform standard maximum likelihood linear regression (MLLR) and feature space MLLR (fMLLR) and their extensions, with notable word error rate (WER) reductions in a per-utterance unsupervised adaptation manner.

The paper proposes and analyzes an adaptive p-persistent-based (APP) medium access control (MAC) scheme for IEEE 802.11 WLAN. The APP MAC scheme intends to support delay fairness for every station in each access, denoting small delay variance. It differentiates permission probabilities of transmission for stations which are incurred with various packet delays. This permission probability is designed as a function of the numbers of retransmissions and re-backoffs so that stations with larger packet delay are endowed with higher permission probability. Also, the scheme is analyzed by a Markov-chain analysis, where the collision probability, the system throughput, and the average delay are successfully obtained. Numerical results show that the proposed APP MAC scheme can attain lower mean delay and higher mean throughput. In the mean time, simulation results are given to justify the validity of the analysis, and also show that the APP MAC scheme can achieve more delay fairness than conventional algorithms.

A number of parallel applications run on a high-performance computing (HPC) system simultaneously. Job mapping and scheduling become crucial to improve system utilization, because fragmentation prevents an incoming job from being assigned even if there are enough compute nodes unused. Wireless supercomputers and datacenters with free-space optical (FSO) terminals have been proposed to replace the conventional wired interconnection so that a diverse application workload can be better supported by changing their network topologies. In this study we firstly present an efficient job mapping by swapping the endpoints of FSO links in a wireless HPC system. Our evaluation shows that an FSO-equipped wireless HPC system can achieve shorter average queuing length and queuing time for all the dispatched user jobs. Secondly, we consider the use of a more complicated and enhanced scheduling algorithm, which can further improve the system utilization over different host networks, as well as the average response time for all the dispatched user jobs. Finally, we present the performance advantages of the proposed wireless HPC system under more practical assumptions such as different cabinet capacities and diverse subtopology packings.

Datacenter growth in traffic and scale is driving innovations in constructing tightly-coupled facilities with low-latency communication for different specific applications. A famous custom design is rackscale (RS) computing by gathering key server resource components into different resource pools. Such a resource-pooling implementation requires a new software stack to manage resource discovery, resource allocation and data communication. The reconfiguration of interconnection networks on their components is potentially needed to support the above demand in RS. In this context as an evolution of the original RS architecture the inter-rackscale (IRS) architecture, which disaggregates hardware components into different racks according to their own areas, has been proposed. The heart of IRS is to use a limited number of free-space optics (FSO) channels for wireless connections between different resource racks, via which selected pairs of racks can communicate directly and thus resource-pooling requirements are met without additional software management. In this study we evaluate the influences of FSO links on IRS networks. Evaluation results show that FSO links reduce average communication hop count for user jobs, which is close to the best possible value of 2 hops and thus provides comparable benchmark performance to that of the counterpart RS architecture. In addition, if four FSO terminals per rack are allowed, the CPU/SSD (GPU) interconnection latency is reduced by 25.99% over Fat-tree and by 67.14% over 2-D Torus. We also present the advantage of an FSO-equipped IRS system in average turnaround time of dispatched jobs for given sets of benchmark workloads.

In this work, we propose a new automatic speech recognition (ASR) system based on feature learning and an end-to-end training procedure for air traffic control (ATC) systems. The proposed model integrates the feature learning block, recurrent neural network (RNN), and connectionist temporal classification loss to build an end-to-end ASR model. Facing the complex environments of ATC speech, instead of the handcrafted features, a learning block is designed to extract informative features from raw waveforms for acoustic modeling. Both the SincNet and 1D convolution blocks are applied to process the raw waveforms, whose outputs are concatenated to the RNN layers for the temporal modeling. Thanks to the ability to learn representations from raw waveforms, the proposed model can be optimized in a complete end-to-end manner, i.e., from waveform to text. Finally, the multilingual issue in the ATC domain is also considered to achieve the ASR task by constructing a combined vocabulary of Chinese characters and English letters. The proposed approach is validated on a multilingual real-world corpus (ATCSpeech), and the experimental results demonstrate that the proposed approach outperforms other baselines, achieving a 6.9% character error rate.

The existing carrier sensing multiple access (CSMA) based wireless networks cannot realize the capture effect functionality. Consequently, transmitters within the physical carrier sensing (PCS) range of a receiver cause interference to its reception, which is referred to as the pseudo capture effect. Such interference severely degrades the system performance because the default PCS range is usually quite large. Therefore the PCS range should be adjusted to reduce the packet loss caused by pseudo capture effect. In order to guide the optimal PCS range setting, a modified p-persistent model is proposed in this paper to investigate the throughput of CSMA-based networks considering pseudo capture effect. Simulation results show that the proposed model accurately evaluates the influence of pseudo capture effect. By utilizing the model, we observe that the optimal PCS range considering pseudo capture effect is smaller than the case without considering its impact.

Multi-FPGA systems have gained attention because of their high performance and power efficiency. A multi-FPGA system called Flow-in-Cloud (FiC) is currently being developed as an accelerator of multi-access edge computing (MEC). FiC consists of multiple mid-range FPGAs tightly connected by high-speed serial links. Since time-critical jobs are assumed in MEC, a circuit-switched network with static time-division multiplexing (STDM) switches has been implemented on FiC. This paper investigates techniques of enhancing the interconnection performance of FiC. Unlike switching fabrics for Network on Chips or parallel machines, economical multi-FPGA systems, such as FiC, use Xilinx Aurora IP and FireFly cables with multiple lanes. We adopted the link aggregation and the slot distribution for using multiple lanes. To mitigate the bottleneck between an STDM switch and user logic, we also propose a multi-ejection STDM switch. We evaluated various combinations of our techniques by using three practical applications on an FiC prototype with 24 boards. When the number of slots is large and transferred data size is small, the slot distribution was sometimes more effective, while the link aggregation was superior for other most cases. Our multi-ejection STDM switch mitigated the bottleneck in ejection ports and successfully reduced the number of time slots. As a result, by combining the link aggregation and multi-ejection STDM switch, communication performance improved up to 7.50 times with few additional resources. Although the performance of the fast Fourier transform with the highest communication ratio could not be enhanced by using multiple boards when a lane was used, 1.99 times performance improvement was achieved by using 8 boards with four lanes and our multi-ejection switch compared with a board.

Due to recent technology progress based on big-data processing, many applications present irregular or unpredictable communication patterns among compute nodes in high-performance computing (HPC) systems. Traditional communication infrastructures, e.g., torus or fat-tree interconnection networks, may not handle well their matchmaking problems with these newly emerging applications. There are already many communication-efficient application mapping algorithms for these typical non-random network topologies, which use nearby compute nodes to reduce the network distances. However, for the above unpredictable communication patterns, it is difficult to efficiently map their applications onto the non-random network topologies. In this context, we recommend using random network topologies as the communication infrastructures, which have drawn increasing attention for the use of HPC interconnects due to their small diameter and average shortest path length (ASPL). We make a comparative study to analyze the impact of application mapping performance on non-random and random network topologies. We propose using topology embedding metrics, i.e., diameter and ASPL, and list several diameter/ASPL-based application mapping algorithms to compare their job scheduling performances, assuming that the communication pattern of each application is unpredictable to the computing system. Evaluation with a large compound application workload shows that, when compared to non-random topologies, random topologies can reduce the average turnaround time up to 39.3% by a random connected mapping method and up to 72.1% by a diameter/ASPL-based mapping algorithm. Moreover, when compared to the baseline topology mapping method, the proposed diameter/ASPL-based topology mapping strategy can reduce up to 48.0% makespan and up to 78.1% average turnaround time, and improve up to 1.9x system utilization over random topologies.

In parallel computing systems, the interconnection network forms the critical infrastructure which enables robust and scalable communication between hundreds of thousands of nodes. The traditional packet-switched network tends to suffer from long communication time when network congestion occurs. In this context, we explore the use of circuit switching (CS) to replace packet switches with custom hardware that supports circuit-based switching efficiently with low latency. In our target CS network, a certain amount of bandwidth is guaranteed for each communication pair so that the network latency can be predictable when a limited number of node pairs exchange messages. The number of allocated time slots in every switch is a direct factor to affect the end-to-end latency, we thereby improve the slot utilization and develop a network topology generator to minimize the number of time slots optimized to target applications whose communication patterns are predictable. By a quantitative discrete-event simulation, we illustrate that the minimum necessary number of slots can be reduced to a small number in a generated topology by our design methodology while maintaining network cost 50% less than that in standard tori topologies.

This paper proposes a name-based routing mechanism called Routing Guidance Name (RGN) that offers new routing management functionalities within the basic characteristics of CCN. The proposed mechanism names each CCN router. Each router becomes a Data Provider for its name. When a CCN Interest specifies a router's name, it is forwarded to the target router according to the standard mechanism of CCN. Upon receiving an Interest, each router reacts to it according to RGN. This paper introduces a new type of node called a Scheduler which calculates the best routes based on link state information collected from routers. The scheduler performs its functions based on RGN. This paper discusses how the proposed system builds CCN FIB (Forwarding Information Base) in routers. The results of experiments reveal that RGN is more efficient than the standard CCN scheme. It is also shown that the proposal provides mobility support with short delay time. We explain a practical mobile scenario to illustrate the advantages of the proposal.