Presentation
Efficient Runtime Management of Crossbars for Path-based In-Memory Computing
DescriptionThe efficient execution of scientific workloads on contemporary architectures is challenged by energy-intensive computations, such as matrix-vector multiplications (MVM). This energy expenditure arises substantially from the transfer of data between the memory and processing units. Systolic arrays, including those based on in-memory path-based computing, have been proposed to expedite demanding MVM operations, leading to significant energy efficiency. However, the size of matrices in scientific workloads is often much larger than the size of the available systolic arrays. Different mappings of these large matrices to relatively smaller crossbar arrays lead to different switchings of non-volatile memory devices and, hence, differences in energy expenditures. Computing an energy-efficient runtime mapping of MVM computations onto systolic arrays to minimize switching of non-volatile memory devices is a hitherto unexplored challenge.
In this paper, we present a framework named Hamiltonian for efficiently scheduling computations on path-based computing systolic arrays when there are constraints on the number of processing elements. We achieve this by introducing a distance metric between different computations and solving the problem by finding a set of Hamiltonian cycles in a complete graph. We evaluate our framework using ten SuiteSparse matrices, and our experimental results demonstrate that Hamiltonian enhances power efficiency and reduces latency by 30% and 30% on average compared with the previous state-of-the-art.
In this paper, we present a framework named Hamiltonian for efficiently scheduling computations on path-based computing systolic arrays when there are constraints on the number of processing elements. We achieve this by introducing a distance metric between different computations and solving the problem by finding a set of Hamiltonian cycles in a complete graph. We evaluate our framework using ten SuiteSparse matrices, and our experimental results demonstrate that Hamiltonian enhances power efficiency and reduces latency by 30% and 30% on average compared with the previous state-of-the-art.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby
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