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Existing SR algorithms rely on deep learning models with significant memory requirements, so they have yet to be deployed on mobile devices and instead operate in the cloud to achieve feasible inference time. This shortcoming prevents existing SR methods from being used in applications that require near real-time latency. In this work, we demonstrate state-of-the-art latency and accuracy for on-device super-resolution using a novel hybrid architecture called SplitSR and a novel lightweight residual block called SplitSRBlock. The SplitSRBlock supports channel-splitting, allowing the residual blocks to retain spatial information while reducing the computation in the channel dimension. SplitSR has a hybrid design consisting of standard convolutional blocks and lightweight residual blocks, allowing people to tune SplitSR for their computational budget. We evaluate our system on a low-end ARM CPU, demonstrating both higher accuracy and up to 5\u00d7 faster inference than previous approaches. We then deploy our model onto a smartphone in an app called ZoomSR to demonstrate the first-ever instance of on-device, deep learning-based SR. We conducted a user study with 15 participants to have them assess the perceived quality of images that were post-processed by SplitSR. Relative to bilinear interpolation --- the existing standard for on-device SR --- participants showed a statistically significant preference when looking at both images (Z=-9.270, p&lt;0.01) and text (Z=-6.486, p&lt;0.01).<\/jats:p>","DOI":"10.1145\/3448104","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T18:56:41Z","timestamp":1617130601000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["SplitSR"],"prefix":"10.1145","volume":"5","author":[{"given":"Xin","family":"Liu","sequence":"first","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}]},{"given":"Yuang","family":"Li","sequence":"additional","affiliation":[{"name":"BUPT &amp; University of Washington, Seattle, WA, USA"}]},{"given":"Josh","family":"Fromm","sequence":"additional","affiliation":[{"name":"University of Washington &amp; OctoML, Seattle, WA, USA"}]},{"given":"Yuntao","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua Univiersity &amp; University of Washington, Seattle, WA, USA"}]},{"given":"Ziheng","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Washington &amp; OctoML, Seattle, WA, USA"}]},{"given":"Alex","family":"Mariakakis","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, Canada"}]},{"given":"Shwetak","family":"Patel","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , 2016 . 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