|Author||Venkatesan, Preethi ♦ Horowitz, Mark ♦ Qadeer, Wajahat ♦ Shacham, Ofer ♦ Kozyrakis, Christos ♦ Hameed, Rehan|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|Abstract||General-purpose processors, while tremendously versatile, pay a huge cost for their flexibility by wasting over 99% of the energy in programmability overheads. We observe that reducing this waste requires tuning data storage and compute structures and their connectivity to the data-flow and data-locality patterns in the algorithms. Hence, by backing off from full programmability and instead targeting key data-flow patterns used in a domain, we can create efficient engines that can be programmed and reused across a wide range of applications within that domain. We present the Convolution Engine (CE)---a programmable processor specialized for the convolution-like data-flow prevalent in computational photography, computer vision, and video processing. The CE achieves energy efficiency by capturing data-reuse patterns, eliminating data transfer overheads, and enabling a large number of operations per memory access. We demonstrate that the CE is within a factor of 2--3× of the energy and area efficiency of custom units optimized for a single kernel. The CE improves energy and area efficiency by 8--15× over data-parallel Single Instruction Multiple Data (SIMD) engines for most image processing applications.<!-- END_PAGE_1 -->|
|Description||Affiliation: Google, Mountain View, CA (Shacham, Ofer) || Palo Alto, CA (Qadeer, Wajahat; Hameed, Rehan) || Stanford University, Stanford, CA (Kozyrakis, Christos; Horowitz, Mark) || Intel Corporation, Santa Clara, CA (Venkatesan, Preethi)|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||Communications of the ACM (CACM)|
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