Operation Snapdragon Research Paper
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Your Guide to Qualcomm Snapdragon SoCs - Gary Explains
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In any event, the SoCs offered impressive 4G support, topping out at 2Gbps. This also marked the first time we saw Qualcomm offer dedicated machine learning hardware, as its Hexagon Tensor Accelerator is a bit of silicon that forms part of the Hexagon DSP. So, machine learning tasks like voice recognition, speech-to-text, and more should be faster and more power-efficient. Qualcomm also concentrated on multimedia in a big way with the series, starting with camera support. Gaming was another big focus area for the firm with this generation of chipsets, which saw the company introduce the Snapdragon Elite Gaming suite of features for the first time.
Another notable addition in this generation was the FastConnect suite, as Qualcomm decided to brand its wireless connectivity feature-set. The FastConnect platform includes Bluetooth 5. Other key features include Quick Charge 4 Plus, a voice assistant accelerator, aptX Adaptive audio for more resilient wireless audio, and ultrasonic fingerprint support. Qualcomm also launched the Snapdragon in , debuting in the Poco X3 Pro. This is effectively a Snapdragon Plus with a couple of very minor changes. Either way, the Snapdragon is aimed at the mid-range in , giving more graphical power and more impressive multimedia capabilities than typical mid-range chipsets.
This time there was no doubting that the Snapdragon was a world-class performer packed to the gills with features while also being the fastest Android phone processor around. Unfortunately, the biggest reported problem is the price. Several sources point to a steep price increase from the Snapdragon series to the Snapdragon For the most part, this has resulted in manufacturers needing to pass this cost to consumers as well, with even the likes of OnePlus and Xiaomi offering drastic price leaps. Qualcomm bundled a separate 5G modem X55 with every Snapdragon SoC, which means that even Snapdragon phones in markets without 5G have the high-speed modem inside them. On the one hand, this means the device is ready for 5G when it comes to your market — provided it has other 5G components too.
In any event, the Snapdragon offers a similar triple power domain CPU arrangement as the series. So, that means four Cortex-A77 cores — one prime core and three medium cores — and four Cortex-A55 cores for efficiency. Meanwhile, the Snapdragon Plus arrived in July and cranked the prime core to 3. Otherwise, the two chips are essentially identical. Both chipsets still have some of the most impressive camera features in a smartphone processor to date. Other noteworthy features include aptX Voice for voice calls over Bluetooth, mmWave and sub-6Ghz 5G, Quick Charge 4 Plus, an AI engine that delivers twice the performance of its predecessor, and support for a Hz refresh rate. The only real upgrade is a clock speed boost for the prime core up to 3.
It looks like the chipset is being positioned as the silicon of choice for upper-mid range phones and affordable flagships in Did you know: The Snapdragon series is the first flagship silicon to offer GPU driver updates via app stores, sidestepping the traditional OTA update process. An integrated modem generally results in better power efficiency, so expect 5G connectivity to be less battery-hungry on Snapdragon phones. Another notable upgrade is the CPU, which still maintains an octa-core three-tier arrangement. The X1 was designed to prioritize power over efficiency, with the aim of closing the gap to Apple, so expect plenty of grunt from it.
Nevertheless, this chipset still makes a very strong argument for being the best Android flagship processor on the market. Did we miss anything? Sound off in the comments below! History of the Qualcomm Snapdragon series: World-class Android processors From pre series days to the Snapdragon , we take a look at the history of Qualcomm's flagship chipsets. By Hadlee Simons Editor. He has over a decade of experience working in the tech journalism space. When he's not working, he's gaming, watching motorsport, or running. He'll get back on the jiu-jitsu mat when this pandemic is over.
Everything you need to know about smartphone chipsets Before the Snapdragon series: Sx and Snapdragon , , Laying the foundation. Snapdragon and Enter the bit era. Snapdragon Back to basics. Snapdragon A blueprint for the future. Snapdragon Still powerful today. Snapdragon and Plus: A return to mid-year refreshes. Snapdragon series: The high cost of 5G. Snapdragon 5G is the new normal. Snapdragon CPU 1x 2.
Features Qualcomm, Qualcomm Snapdragon. Snapdragon 2x Qualcomm Kryo 2. This is an important milestone as mobile devices are beginning to offer the performance that is sufficient for running many standard deep learning models, even without any special adaptations or modifications. However, it is unclear if the same situation will persist in the future: to reach the performance level of the 4th generation NPUs, the speed of AI inference on GPUs should be increased by times. This cannot be easily done without introducing some major changes to their micro-architecture, which will also affect the entire graphics pipeline.
It therefore is likely that all major chip vendors will switch to dedicated neural processing units in the next SoC generations. Accelerating deep learning inference with the mid-range e. Even worse results will be obtained on the entry-level GPUs since they come with additional computational constraints. One should, however, note that the power consumption of GPU inference is usually 2 to 4 times lower than the same on the CPU. Hence this approach might still be advantageous in terms of overall energy efficiency. By switching to their custom vendor implementation, one can achieve up to 10 times speed-up for many deep learning architectures: e.
These two SoCs are showing nearly identical results in all int-8 tests, and are slightly faster than the Kirin , Helio P90 and the standard Snapdragon As claimed by Qualcomm, the performance of the Hexagon DSP has approximately doubled over the previous-generation Hexagon The latter, together with its derivatives Hexagon and , is currently present in Qualcomm's mid-range chipsets. One should note that there exist multiple revisions of the Hexagon , as well as several versions of its drivers. As mobile GPUs are primarily designed for floating-point computations, accelerating quantized AI models with them is not very efficient in many cases. It showed an overall performance similar to that of the Hexagon DSP in the Snapdragon , though the inference results of both chips are heavily dependent on the running model.
As a result, using GPUs for quantized inference on the mid-range and low-end devices might be reasonable only to achieve a higher power efficiency. Floating-point vs. Quantized Inference One can see that the above results are split into two categories with quite different performance numbers, and the reasonable question here would be what inference type is more appropriate for being used on smartphones. Unfortunately, there has been a lot of confusion with these two types in the mobile industry, including a number of incorrect statements and invalid comparisons.
We therefore decided to devote a separate section to them and describe and compare their benefits and disadvantages. We divided the discussion into three sections: the first two are describing each inference type separately, while the last one compares them directly and makes suggestions regarding their application. Floating-Point Inference Advantages: The model is running on mobile devices in the same format as it was originally trained on the server or desktop with standard machine learning libraries.
No special conversion, changes or re-training is needed; thus one gets the same accuracy and performance as on the desktop or server environment. Disadvantages: Many recent state-of-the-art deep learning models, especially those that are working with high-resolution image transformations, require more than gigabytes of RAM and enormous computational resources for data processing that are not available even in the latest high-end smartphones. Thus, running such models in their original format is infeasible, and they should be first modified to meet the hardware resources available on mobile devices. Quantized Inference Advantages: The model is first converted from a bit floating point type to int-8 format.
This reduces its size and RAM consumption by a factor of 4 and potentially speeds up its execution by times. Since integer computations consume less energy on many platforms, this also makes the inference more power efficient, which is critical in the case of smartphones and other portable electronics. Disadvantages: Reducing the bit-width of the network weights from 16 to 8 bits leads to accuracy loss: in some cases, the converted model might show only a small performance degradation, while for some other tasks the resulting accuracy will be close to zero.
Although a number of research papers dealing with network quantization were presented by Qualcomm and Google all showing decent accuracy results for many image classification models, there is no general recipe for quantizing arbitrary deep learning architectures. Thus, quantization is still more of a research topic, without working solutions for many AI-related tasks e. Besides that, many quantization approaches require the model to be retrained from scratch, preventing the developers from using available pre-trained models provided together with all major research papers. Comparison As one can see, there is always a trade-off between using one model type or another: floating-point models will always show better accuracy since they can be simply initialized with the weights of the quantized model and further trained for higher accuracy , while integer models yield faster inference.
