Probabilistic Value-Deviation-Bounded Source-Dependent Bit-Level Channel Adaptation for Approximate Communication
نویسندگان
چکیده
منابع مشابه
Probabilistic Value-Deviation-Bounded Integer Codes for Approximate Communication
M computing systems are designed to prevent errors in computation, memory, and communication. Guarding against errors however requires energy, temporal redundancy, or spatial redundancy and therefore consumes resources. But not all systems need to be free of errors: In some systems, either by explicit design or by the nature of the problems they solve, system output quality degrades gracefully ...
متن کاملValue-Deviation-Bounded Serial Data Encoding for Energy-Efficient Approximate Communication
Transferring data between ICs accounts for a growing proportion of system power in wearable and mobile systems. Reducing signal transitions reduces the dynamic power dissipated in this data transfer, but traditional approaches cannot be applied when the transfer interfaces are serial buses. To address this challenge, we present a family of optimal value-deviation-bounded approximate serial enco...
متن کاملLarge Deviation Methods for Approximate Probabilistic Inference
We study two-layer belief networks of binary random variables in which the conditional probabilities Pr[childjparents] depend monotonically on weighted sums of the parents. In large networks where exact probabilistic inference is intractable, we show how to compute upper and lower bounds on many probabilities of interest. In particular, using methods from large deviation theory, we derive rigor...
متن کاملOn Iterative Source-channel Decoding for Variable-length Encoded Markov Sources Using a Bit-level Trellis
In this paper we present a novel bit-level soft-input / softoutput decoding algorithm for variable-length encoded packetized Markov sources transmitted over wireless channels. An interesting feature of the proposed approach is that symbol-based source statistics in form of transition probabilities of the Markov source are exploited as a-priori information on a bit-level trellis. When additional...
متن کاملApproximate Probabilistic Inference via Word-Level Counting
Probabilistic inference on large and uncertain data sets is increasingly being used in a wide range of applications. It is well-known that probabilistic inference is polynomially inter-reducible to model counting (Roth 1996). In a recent line of work, it has been shown (Chakraborty, Meel, and Vardi 2013; Chakraborty et al. 2014; Ermon et al. 2014; Ermon et al. 2013) that one can strike a fine b...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Computers
سال: 2020
ISSN: 0018-9340,1557-9956,2326-3814
DOI: 10.1109/tc.2020.3031494