Hi Cartagena,
I don't believe there is any evidence that Qualcomm uses Akida, although I wish they did.
They have their in-house Hexagon:
https://www.qualcomm.com/news/onq/2...ile-computing-performance-for-windows-laptops
https://www.theregister.com/2022/11/15/qualcomm_snapdragon_8_gen_2/?td=readmore
Qualcomm pushes latest Arm-powered Snapdragon chip amid bitter license fight
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he Snapdragon 8 Gen 2 system-on-chip features eight off-the-shelf cores from Arm, which is locked in a bitter legal fight with Qualcomm over licenses and contracts.
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his includes an AI acceleration engine that is, we're told, up to 4.35 times faster than the previous generation, and with a potential 60 percent increase in performance-per-watt, depending on how it's used. This unit can be used to speed up machine-learning tasks on the device without any outside help, such as object recognition, and real-time spoken language translation and transcription. The dual-processor engine can handle as low as INT4 precision for AI models that don't need a lot of precision but do need it done fast on battery power, which the 4-bit integer format can afford developers, according to Qualcomm.
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alcomm is pushing the INT4 capabilities as a precision ideal for modern mobile apps. It said a cross-platform Qualcomm AI Studio is due to be made available in preview form in the first half of next year that will optimize developers' models for this precision as well as other formats. This studio looks like a typical IDE in which programmers can organize their training workflows.
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he SoC supports up to 200MP image capture and 8K HDR video capture in 10-bit HDR, according to the specifications. Qualcomm said it worked with Samsung and Sony to develop large sensors that the 8 Gen 2 can handle. There are direct paths in the chip that link the Hexagon AI engines to, say, the image-processing units so that pictures and video can be manipulated more efficiently.
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he processor, according to Qualcomm, can also be made to reduce the amount of data read and written during neural network inference – which saves power – by breaking input data into not just tiles as other chipsets do, but micro tiles that apparently do a better job of cutting down information transfer.
This is from a quote in a now-deleted Stable genius post on TSEx:
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ualcomm uses all of the Snapdragon SoC’s processing elements for AI processing and calls the combination of these processing elements the “AI engine.” Among the enhancements incorporated into the AI engine was a dedicated power plane and a doubling of the tensor processing cores within the Hexagon processor. The result is a 4.35x improvement in performance and an equally impressive 60% improvement in performance per watt efficiency. Qualcomm also added support for transformer neural network models which are critical for applications like natural language processing (NLP) for speech-to-text and text-to-speech translation. The Hexagon can splice the neural NLP model into smaller elements to run on micro tiles allowing for more efficient use of the processing cores. Additionally, Qualcomm added support for Int4 data structures. In many cases, this lower precision data structure can be used by neural network models like computational photography image enhancement without a noticeable loss of accuracy while improving speed and power efficiency. The end result is faster and more efficient processing of neural network models.
This is from a 2021 Qualcomm patent application:
WO2023049655A1 TRANSFORMER-BASED ARCHITECTURE FOR TRANSFORM CODING OF MEDIA 2021-09-27
View attachment 43051
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ystems and techniques are described herein for processing media data using a neural network system. For instance, a process can include obtaining a latent representation of a frame of encoded image data and generating, by a plurality of decoder transformer layers of a decoder sub-network using the latent representation of the frame of encoded image data as input, a frame of decoded image data. At least one decoder transformer layer of the plurality of decoder transformer layers includes: one or more transformer blocks for generating one or more patches of features and determine self-attention locally within one or more window partitions and shifted window partitions applied over the one or more patches; and a patch un-merging engine for decreasing a respective size of each patch of the one or more patches.
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[0075] The SOC 100 may also include additional processing blocks tailored to specific functions, such as a GPU 104, a DSP 106, a connectivity block 110, which may include fifth generation (5G) connectivity, fourth generation long term evolution (4G LTE) connectivity, Wi-Fi connectivity, USB connectivity, Bluetooth connectivity, and the like, and a multimedia processor 112 that may, for example, detect and recognize gestures. In one implementation, the NPU is implemented in the CPU 102, DSP 106, and/or GPU 104. The SOC 100 may also include a sensor processor 114, image signal processors (ISPs) 116, and/or navigation module 120, which may include a global positioning system.
Snapdragon8_2Hexagon
Snapdragon 8 Gen 2 deep dive: Everything you need to know (androidauthority.com)
All you'll ever need to know about Qualcomm's latest Snapdragon 8 Gen 2 mobile platform for next-gen phones can be found here.
www.androidauthority.com
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ualcomm doubled the physical link between the image signal processor (ISP), Hexagon DSP, and Adreno GPU, driving higher bandwidth and lowering latency. This allows the Snapdragon 8 Gen 2 to run much more powerful machine-learning tasks on imaging data right off the camera sensor. RAW data, for instance, can be passed directly to the DSP/AI Engine for imaging workloads, or Qualcomm can use the link to upscale low-res gaming scenarios to assist with GPU load balancing.