Learn More
—In this paper, we present a novel algorithmic noise-tolerance (ANT) technique referred to as reduced precision redundancy (RPR). RPR requires a reduced precision replica whose output can be employed as the corrected output in case the original system computes erroneously. When combined with voltage overscaling (VOS), the resulting soft digital signal(More)
Orthogonal matching pursuit (OMP) is a greedy search algorithm popularly being used for the recovery of compressive sensed sparse signals. In this correspondence, we show that if the isometry constant &#x03B4;<i>K</i>+1 of the sensing matrix &#x03A6; satisfies &#x03B4;<i>K</i>+1 &lt;; 1/(1/&#x221A;<i>K</i>+1) then the OMP algorithm can perfectly recover(More)
In this paper, we propose an algorithm referred to as multipath matching pursuit (MMP) that investigates multiple promising candidates to recover sparse signals from compressed measurements. Our method is inspired by the fact that the problem to find the candidate that minimizes the residual is readily modeled as a combinatoric tree search problem and the(More)
As a greedy algorithm to recover sparse signals from compressed measurements, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we introduce an extension of the OMP for pursuing efficiency in reconstructing sparse signals. Our approach, henceforth referred to as generalized OMP (gOMP), is literally a(More)
In this paper, we present a near ML-achieving sphere decoding algorithm that reduces the number of search operations in the sphere-constrained search. Specifically, by adding a probabilistic noise constraint on top of the sphere constraint, a more stringent necessary condition is provided, particularly at an early stage, and, hence, branches unlikely to be(More)
In this paper, we present energy-efficient soft error (E-tolerant techniques for digital signal processing (DSP) systems. The proposed technique, referred to as algorithmic soft error-tolerance (ASET), employs an low-complexity estimator of a main DSP block to guarantee reliability in presence of soft errors either in the MDSP or the estimator. For FIR(More)
In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable(More)
As a paradigm for reconstructing sparse signals using a set of under sampled measurements, compressed sensing has received much attention in recent years. In identifying the sufficient condition under which the perfect recovery of sparse signals is ensured, a property of the sensing matrix referred to as the restricted isometry property (RIP) is popularly(More)