Approximate computing is the idea that systems can gain performance and energy efficiency if they expend less effort on producing a “perfect” answer. Approximate computing techniques propose various ways of exposing and exploiting accuracy– efficiency trade-offs. We present a taxonomy that classifies approximate computing techniques according to their most salient features: compute vs. data, de...

Enhancing result-accuracy in approximate computing (AC) based real-time systems, without violating power constraints of the underlying hardware, is a challenging problem. Execution such AC applications can be split into two parts: (i) the mandatory part , execution which provides result accepta...

Microelectrode arrays (MEAs) are essential equipment in neuroscience for studying the nervous system’s behavior and organization. MEAs of parallel electrodes that work by sensing extracellular potential neurons their proximity. Processing data streams acquired from is a computationally intensive task requiring parallelization. It performed using complex signal processing algorithms architectura...

Consider I ⊂ C[x1, . . . , xm], a zero dimensional complete intersection ideal, with I = (f1, . . . , fm). Assume that I has clusters of roots, each cluster of radius at most ε in the ∞-norm. We compute the “approximate radical” of I, i.e. an ideal which contains one root for each cluster, corresponding to the center of gravity of the points in the cluster, up to an error term asymptotically bo...

A divide-and-conquer method for computing approximate eigenvalues and eigenvectors of a block tridiagonal matrix is presented. In contrast to a method described earlier [W. N. Gansterer, R. C. Ward, and R. P. Muller, ACM Trans. Math. Software, 28 (2002), pp. 45–58], the off-diagonal blocks can have arbitrary ranks. It is shown that lower rank approximations of the off-diagonal blocks as well as...