Topic
Latency (engineering)
About: Latency (engineering) is a research topic. Over the lifetime, 7278 publications have been published within this topic receiving 115409 citations. The topic is also known as: lag.
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TL;DR: Development of the auditory P300 as a marker of neurobiological processes in aging and dementia is discussed, and the rate of change in P3 latency may reflect accelerated senescence in Alzheimer's disease.
Abstract: A longitudinal study of the changes in latency of the P300 (P3) wave of the auditory event-related brain potential was undertaken in a group of 18 thoroughly screened and diagnosed possible and probable Alzheimer's disease (pAD) patients and 15 normal controls. On initial recording, P3 latency was significantly prolonged in the pAD group by more than 1.5 standard deviations (40 msec) beyond the normal group. Over the course of the next 3 years, the rate of increase in P3 latency was significantly greater for the patient group than for the controls. The rate of change in P3 latency may reflect accelerated senescence in Alzheimer's disease. Development of the auditory P300 as a marker of neurobiological processes in aging and dementia is discussed.
78 citations
TL;DR: This paper presents several simple design techniques that can reduce such latency penalty caused by soft-decision ECCs, and suggests that the latency can be reduced by up to 85.3%.
Abstract: With the aggressive technology scaling and use of multi-bit per cell storage, NAND flash memory is subject to continuous degradation of raw storage reliability and demands more and more powerful error correction codes (ECC). This inevitable trend makes conventional BCH code increasingly inadequate, and iterative coding solutions such as LDPC codes become very natural alternative options. However, these powerful coding solutions demand soft-decision memory sensing, which results in longer on-chip memory sensing latency and memory-to-controller data transfer latency. Leveraging well-established lossless data compression theories, this paper presents several simple design techniques that can reduce such latency penalty caused by soft-decision ECCs. Their effectiveness have been well demonstrated through extensive simulations, and the results suggest that the latency can be reduced by up to 85.3%.
78 citations
20 Jun 2016
TL;DR: The design and deployment of WiFiSeer, a framework to measure and characterize WiFi latency at large scale, and the measurement results quantitatively confirm some anecdotal perceptions about impacting factors and disapprove others.
Abstract: WiFi latency is a key factor impacting the user experience of modern mobile applications, but it has not been well studied at large scale. In this paper, we design and deploy WiFiSeer, a framework to measure and characterize WiFi latency at large scale. WiFiSeer comprises a systematic methodology for modeling the complex relationships between WiFi latency and a diverse set of WiFi performance metrics, device characteristics, and environmental factors. WiFiSeer was deployed on Tsinghua campus to conduct a WiFi latency measurement study of unprecedented scale with more than 47,000 unique user devices. We observe that WiFi latency follows a long tail distribution and the 90th (99th) percentile is around 20 ms (250 ms). Furthermore, our measurement results quantitatively confirm some anecdotal perceptions about impacting factors and disapprove others. We deploy three practical solutions for improving WiFi latency in Tsinghua, and the results show significantly improved WiFi latencies. In particular, over 1,000 devices use our AP selection service based on a predictive WiFi latency model for 2.5 months, and 72% of their latencies are reduced by over half after they re-associate to the suggested APs.
78 citations
TL;DR: Data obtained from reaction time (RT) measurements as well as from experiments employing masking paradigms suggest that high spatial frequency gratings are perceived with some delay in comparison with low spatial frequency ones, supported by records of human visually evoked response (VER) to sinusoidal gratings.
78 citations
Posted Content•
TL;DR: A first-pass Recurrent Neural Network Transducer model and a second-pass Listen, Attend, Spell (LAS) rescorer that surpasses a conventional model in both quality and latency is developed and found that RNN-T+LAS offers a better WER and latency tradeoff compared to a conventional models.
Abstract: Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e., the time the hypothesis is finalized after the user stops speaking. In this paper, we develop a first-pass Recurrent Neural Network Transducer (RNN-T) model and a second-pass Listen, Attend, Spell (LAS) rescorer that surpasses a conventional model in both quality and latency. On the quality side, we incorporate a large number of utterances across varied domains to increase acoustic diversity and the vocabulary seen by the model. We also train with accented English speech to make the model more robust to different pronunciations. In addition, given the increased amount of training data, we explore a varied learning rate schedule. On the latency front, we explore using the end-of-sentence decision emitted by the RNN-T model to close the microphone, and also introduce various optimizations to improve the speed of LAS rescoring. Overall, we find that RNN-T+LAS offers a better WER and latency tradeoff compared to a conventional model. For example, for the same latency, RNN-T+LAS obtains a 8% relative improvement in WER, while being more than 400-times smaller in model size.
78 citations