Achieving high-performance while reducing power consumption is the key question as tech-nology scaling is reaching its limits. It is well-accepted that application-specific custom hard-ware can achieve orders of magnitude improvements in efficiency. The question is whether such efficiency can be maintained while providing enough flexibility to implement a broad class of op-erations. In this paper, we aim to answer this question for the domain of matrix computations. We propose a design of a novel linear algebra processor and demonstrate that it can achieve orders of magnitude improvements in efficiency for matrix-matrix multiplication, an operation that is indicative for a broad class of matrix computations. A feasibility study shows that 4...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...
Using super-resolution techniques to estimate the direction that a signal arrived at a radio receive...
Matrix multiplication is required for a wide variety of applications, including data mining, linear ...
textIn the past, we could rely on technology scaling and new micro-architectural techniques to impro...
UnrestrictedRecently, high-end computing systems have been introduced that employ reconfigurable har...
ABSTRACT: In this paper, we have proposed one designs for matrix-matrix multiplication. The one desi...
This report has been developed over the work done in the deliverable [Nava94] There it was shown tha...
Technology scaling trends have enabled the exponential growth of computing power. However, the perfo...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
A current trend in high-performance computing is to decompose a large linear algebra problem into ba...
Some level-2 and level-3 Distributed Basic Linear Algebra Subroutines (DBLAS) that have been impleme...
Abstract—Scientific programmers often turn to vendor-tuned Basic Linear Algebra Subprograms (BLAS) t...
Abstract. Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major buildin...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
We have repurposed Google Tensor Processing Units (TPUs), application-specific chips developed for m...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...
Using super-resolution techniques to estimate the direction that a signal arrived at a radio receive...
Matrix multiplication is required for a wide variety of applications, including data mining, linear ...
textIn the past, we could rely on technology scaling and new micro-architectural techniques to impro...
UnrestrictedRecently, high-end computing systems have been introduced that employ reconfigurable har...
ABSTRACT: In this paper, we have proposed one designs for matrix-matrix multiplication. The one desi...
This report has been developed over the work done in the deliverable [Nava94] There it was shown tha...
Technology scaling trends have enabled the exponential growth of computing power. However, the perfo...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
A current trend in high-performance computing is to decompose a large linear algebra problem into ba...
Some level-2 and level-3 Distributed Basic Linear Algebra Subroutines (DBLAS) that have been impleme...
Abstract—Scientific programmers often turn to vendor-tuned Basic Linear Algebra Subprograms (BLAS) t...
Abstract. Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major buildin...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
We have repurposed Google Tensor Processing Units (TPUs), application-specific chips developed for m...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...
Using super-resolution techniques to estimate the direction that a signal arrived at a radio receive...
Matrix multiplication is required for a wide variety of applications, including data mining, linear ...