Acm Algorithm Mind Map

Hegde, K, Tsai, PA, Huang, S, Chandra, V, Parashar, A amp Fletcher, CW 2021, Mind mappings Enabling efficient algorithm-accelerator mapping space search. in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2021. International Conference on Architectural Support for Programming Languages and Operating Systems

Our faculty study algorithms in many diverse areas computational geometry amp topology, graphs, optimization, approximation, randomization, data structures, cryptography and secure computation, economics and computation, complexity theory, foundations of machine learning, and applications to several areas including operations research

The map introduces several basic algorithms, including divide and conquer, dynamic programming, greedy, backtracking, and branch and bound. Suitable for beginners to have a preliminary understanding of algorithms. At the same time, it is equipped with a large number of cases and pictures to help understand the content and ideas of the algorithm.

The key idea is to derive a smooth, differentiable approximation to the otherwise non-smooth, non-convex search space. With a smooth, differentiable approximation, we can leverage efficient gradient-based search algorithms to find high-quality mappings. We extensively compare Mind Mappings to black-box optimization schemes used in prior work.

Mind Mappings dramatically improved the performance of crucial algorithm-accelerator map-space search problem by re-formulating the problem to enable powerful gradient-based search techniques, thus inuencing future works. Proposed solution enabled a target domain-independent approach that generalized to different algorithms and architectures

Hegde, Kartik, Po-An Tsai, Sitao Huang, Vikas Chandra, Angshuman Parashar, and Christopher W. Fletcher. quotMind mappings enabling efficient algorithm-accelerator mapping space search.quot In Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 943-958. 2021.

Request permissions from email160protected. ASPLOS '21, April 19-23, 2021, Virtual, USA Architecture design and algorithm mapping in hardware accelerators. Mapping space search is an important problem, and currently This paper addresses the challenges above by proposing Mind Map-pings, a scalable and automated method to quickly

Modern day computing increasingly relies on specialization to satiate growing performance and efficiency requirements. A core challenge in designing such specialized hardware architectures is how to perform mapping space search, i.e., search for an optimal mapping from algorithm to hardware. Prior work shows that choosing an inefficient mapping can lead to multiplicative-factor efficiency

Mind Mappings Enabling E icient Algorithm-Accelerator Mapping Space Search ASPLOS '21, April 19-23, 2021, Virtual, USA many local minima, making the search even harder. Giv en the hu-

Our representation is motivated by the strong empirical evidence that map representations help users gain and retain knowledge for example, mind maps and knowledge maps have been shown to increase memory recall in students, 11,23 as well as motivation and concentration. 15 We have also found map visualizations enable users to digest