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Arm Accuracy Super Resolution (ASR) is an open-source temporal upscaler that lets game developers render some graphics below a phone’s display resolution and reconstruct them into a higher-resolution image. It may improve performance or reduce graphics workload in demanding, GPU-bound mobile games, but it is not a setting players can switch on for every Android game.

Arm introduced ASR on July 10, 2024, then announced public developer availability at GDC on March 18, 2025. The practical benefit depends on a game integrating it, compatible rendering and device support, and careful tuning.

What ASR changes in a mobile game

A phone’s display resolution is the physical resolution of its panel. Render resolution is the resolution at which the game produces its graphics before they are presented. With ASR, a developer can render selected work at a lower internal resolution, then reconstruct the result for the game’s target output.

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That distinction matters on phones, where high-resolution screens, demanding effects and sustained play compete with limited GPU power, battery capacity and thermal headroom. Rendering fewer pixels can reduce GPU workload and memory-bandwidth pressure. Developers may use that headroom to improve frame rate, lower power use, or retain more expensive effects such as ray tracing. ASR does not remove the cost of those effects, and the final outcome depends on how the game spends the saved resources.

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Arm describes ASR as a mobile-optimized temporal upscaler. It was introduced in July 2024 and made publicly available to developers in March 2025. Arm’s introduction and GDC availability announcement explain the two milestones.

How temporal upscaling reconstructs an image

Unlike a basic spatial scaler that enlarges only the current frame, ASR uses information across frames. Its inputs include the current lower-resolution color image, motion vectors that describe object movement, depth data, camera jitter information and previous-frame history. The algorithm uses these signals to estimate a higher-resolution image while retaining detail that would otherwise require rendering more pixels. Arm’s ASR repository describes its temporal accumulation approach.

A simplified pipeline looks like this:

  1. Render selected game graphics at a lower internal resolution.
  2. Provide ASR with the current image and temporal inputs such as motion vectors and depth.
  3. Reconstruct the image at the target output resolution.
  4. Composite interface elements and other passes in the appropriate order before presentation.

The reconstructed frame is not identical to a natively rendered one in every scene. The quality of motion vectors, depth and render ordering affects the result; missing or inaccurate data can create visible instability.

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What Arm’s performance numbers do—and do not—show

Arm reported a 53% frame-rate increase over native-resolution rendering in a complex scene running on an Immortalis-G720 GPU at 2800×1260. In a separate Unreal Engine “Mori” demonstration, Arm reported up to a 30% FPS improvement. Both figures come from Arm’s demonstrations, not independent tests across retail phones and games. They are not a prediction of the gain a particular player will see. Arm’s announcement gives the benchmark context.

ASR is most promising when the GPU is the bottleneck and the game can trade internal resolution for reconstructed output. A CPU-bound game may gain little because upscaling does not reduce CPU work. ASR itself also consumes GPU resources, so its net value depends on render scale, scene complexity, target frame rate, driver behavior, thermal state and implementation quality. The reported “up to” result is not an average or a guarantee.

Lower rendering cost can reduce power use per frame and may help a phone sustain performance before heat prompts throttling. If a developer instead uses the headroom to raise frame rate or add heavier effects, the battery benefit may shrink or disappear. Arm presents power and thermal benefits as potential outcomes, not fixed battery-life improvements.

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Visual trade-offs developers need to test

Temporal reconstruction can preserve more detail than a simple spatial upscale, but it relies on a coherent history of frames. Fast motion, newly exposed surfaces and effects that do not provide useful motion data can challenge that history. Common issues to inspect include:

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  • Ghost trails behind moving objects or after rapid camera motion.
  • Shimmering or unstable detail on foliage, wires and other thin geometry.
  • Flicker or breakup in particles and transparency effects.
  • Soft or unstable interface elements if UI is included in the upscaling pass rather than composited afterward.
  • Disocclusion artifacts when an object moves and reveals surfaces that were hidden in previous frames.
  • Loss of fine texture detail at aggressive render scales or with poor motion-vector data.

These are integration and tuning concerns, not evidence that every ASR implementation will show every artifact. Developers need to compare the reconstructed image with native rendering across representative gameplay, especially fast movement, transparency-heavy scenes and effects such as reflections and shadows.

ASR’s relationship to FSR and Arm’s neural upscaling

ASR is not an unrelated alternative to AMD FidelityFX Super Resolution 2 (FSR2): Arm says it is derived from FSR 2.2.2 and adapted with mobile hardware in mind. The implementation, optimizations and results should not be assumed to be identical to every FSR2 integration. Arm publishes the code under the MIT license, which lets developers inspect, adapt and integrate it subject to that license’s terms. Open source does not eliminate engine integration, device testing or visual tuning. The repository documents the lineage and license.

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ASR is also distinct from Arm Neural Super Sampling. ASR is a conventional temporal upscaler; it does not require AI hardware. Arm’s later neural-graphics announcements describe a separate AI-based direction associated with future Arm GPUs and neural acceleration. They do not mean that current ASR on existing phones is neural upscaling. See Arm’s neural technology announcement and its Neural Super Sampling overview.

Approach How it works Key trade-off
Native rendering Renders at the target resolution. Provides a useful image-quality reference, but has the highest rendering workload of these options.
Dynamic resolution Changes internal resolution to help meet a performance target. Can manage workload, but the image becomes softer as resolution falls.
Arm ASR Uses temporal inputs to reconstruct a higher-resolution output from lower-resolution rendering. Can improve quality per rendered pixel, but requires accurate inputs and tuning.
AMD FSR2 Temporal upscaling technology from which ASR is derived. Existing integrations and platform behavior vary; ASR and FSR2 should not be presumed interchangeable.
Arm Neural Super Sampling A newer neural-upscaling direction from Arm. Separate from current ASR and tied to newer neural graphics capabilities.

There is no universal ranking among these options. Developers should compare them in the same game, on the same device and at matched frame-rate and image-quality targets.

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Who can integrate ASR now?

ASR is primarily a developer tool, not a consumer app or a phone-wide graphics toggle. Arm documents an Unreal Engine plugin for UE 5.3, 5.4 and 5.5, and a generic library for integration into other engines. Its official learning material describes a Unity plugin as forthcoming, so Unity availability should not be assumed. Consult Arm’s developer hub for its documented paths and the ASR learning material for the Unity qualification. Arm also provides a generic-library integration tutorial.

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Arm optimizes ASR for Arm mobile devices, but that does not establish equal performance or support across every GPU vendor. An Arm CPU alone does not guarantee that a phone’s GPU, graphics API, driver or game supports ASR. Generic library availability likewise is not proof of equivalent acceleration on Adreno, Apple or other GPU architectures.

What an implementation involves

A developer first needs a renderer that can supply the temporal data ASR expects. The team then selects an integration path, configures render resolution and the order of scene, transparency and UI passes, and validates the image and performance on target hardware. This is more than adding a global setting: motion vectors, depth conventions, camera jitter and exposure handling must agree with the integration.

Testing should cover static views, fast camera movement, foliage, particles, reflections, transparency and newly revealed geometry. Teams should profile CPU and GPU time, frame pacing, memory bandwidth and temperature, then compare against native rendering under matched conditions. A robust game may also retain alternatives such as native rendering, dynamic resolution or other supported upscalers for devices or scenes where ASR is not a good fit.

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What players should expect, including Fortnite

Players do not install ASR as a standalone app or turn it on globally in Android settings. A game must integrate the technology and ship it in a build, and the device must support that implementation. To check a particular title, look for an in-game upscaling or graphics option, game patch notes, device-specific graphics menus or official game documentation. An Arm-powered phone by itself does not confirm ASR support.

Arm announced work with Epic on ASR integration for Fortnite mobile. Arm has also cited Fortnite and Infinity Nikki in later material about games using ASR, but those statements do not establish that every player, device or release build exposes an ASR switch. The announcements are evidence of developer activity, not a universal rollout. Arm’s Epic announcement and its later technology announcement provide that context.

When ASR is a sensible choice

ASR is most relevant to a team with a GPU-heavy mobile game, reliable temporal rendering data and the capacity to test across its target devices. It is less compelling when CPU work dominates, the renderer cannot provide suitable inputs, temporal artifacts are unacceptable, or the team cannot validate a diverse device range. A project that already has a well-integrated alternative should evaluate ASR through controlled comparisons rather than assume that its mobile focus guarantees a better result.

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