OnQ Blog

The Snapdragon 610 and 615 Processors: Introducing 4G LTE Integration and 64-bit to the 600 Series

Feb 24, 2014

Qualcomm products mentioned within this post are offered by Qualcomm Technologies, Inc. and/or its subsidiaries.

There is a wise saying that states “good things come in pairs.” Such is the case today, as Qualcomm Technologies Inc.'s (QTI) mobile processor lineup expands with two additions to the Qualcomm® Snapdragon™ 600 processor series: the Snapdragon 615 processor—the world’s first 64-bit eight-core SoC with integrated LTE—and the Snapdragon 610 processor, essentially the same as the Snapdragon 615 processor, but using a quad-core CPU.

We believe everyone, regardless of their budget, deserves to enjoy outstanding mobile user experiences. That is why last year QTI was the first to announce 64-bit computing and the global Qualcomm® RF360 solution for high-volume Android based devices with the Snapdragon 410 processor. Continuing with that trend, the Snapdragon 615 and 610 processors are designed to deliver even more features and benefits for high-end smartphones and tablets. 

The big brother of the pair, the Snapdragon 615 processor, is designed for flexibility, providing device manufacturers with a breadth of options that allow them to differentiate their product while creating compelling, innovative designs. Also, each processor will be pin-to-pin and software compatible with the Snapdragon 410 processor, allowing OEMs to easily leverage their existing designs. Finally, the Snapdragon 615 processor delivers what Chinese OEMs are looking for: Integrated 4G LTE, 64-bit computing, a global RF Front End solution, and an eight-core processor.

Both the Snapdragon 610 and 615 processors include:

  • Global next-generation 4G LTE
    • Integrated third-generation 4G LTE modem supporting Category 4 data rates, designed to provide fantastic speeds for browsing and streaming on the web
    • Qualcomm RF360 Front End solution for a single SKU with all five modes, enabling /network compatibility across the globe

  • Next-generation Adreno 405 graphics
    • Support for cutting-edge mobile graphics features like hardware tessellation and geometry shaders for more detailed, realistic mobile games and visually stunning user interfaces
    • Support for the latest mobile graphics APIs such as OpenGL3.0 and DirectX 11
  • HD 2K display support (2560x1600)
    • Miracast wireless display
    • Native H.265 (HEVC) decode for more power-efficient playback of compressed content
  • ARM Cortex A53 CPUs
    • Customized design cells and caches to improve performance and power efficiency
    • Executing the new ARMv8 instruction set

The Snapdragon 610 and 615 processors are sampling now and expected to be available in commercial devices by the end of 2014.

For more details on the Snapdragon 610 and 615 processors, and to find out about commercial devices once they’re available, please visit our Snapdragon website. You can also get your questions answered in person if you’re in Barcelona for Mobile World Congress this week.  Visit Qualcomm in Hall 3, Stand #3E10.

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Snapdragon

Snapdragon Wear 2100 powers high-end fashion smartwatches at Baselworld

Silicon Valley met Switzerland at this year’s Baselworld, the world’s premier event for the watch and jewelry industry, which celebrated its 100th anniversary this year. Several impressive smartwatches made their debut, all touting the Qualcomm Snapdragon Wear 2100 Platform and all powered by Android Wear 2.0. With this reliable platform and OS developed specifically for wearables, it’s no wonder high-end brands are looking beyond basic wearable functions, and combining style with technology to develop chic smartwatches fit for any lifestyle.

The superior SoC for smartwatches, Snapdragon Wear 2100, is an integrated, ultra-low power sensor hub. It’s 30 percent smaller than previous-generation wearable SoCs, allowing OEMs the freedom to develop thinner, sleeker product designs. And because it uses 25 percent less power than its older sibling (the Snapdragon 400), watchmakers can offer even more features and better designs.

The Snapdragon Wear 2100 comes in both tethered (Bluetooth and Wi-Fi) and connected (3G and 4G LTE) versions. The latter allows wearers to do more with their wearables, from streaming music to sending messages to calling a cab, in tandem with — or even without — having to bring their smartphones along.

Each of the touchscreen smartwatches included in this roundup run Android Wear 2.0, Google’s latest wearable operating system, and can pair with both iOS and Android phones. With Android Wear 2.0, users can personalize their watch faces with chronometer-style complications and create shortcuts to their favorite applications. In addition to the pre-installed Google Fit and calendar apps, more apps can be downloaded directly through the on-watch Google Play store, so wearers can customize their device to their lifestyle.

Android Wear 2.0 brings the Google Assistant to your wrist. Find answers and get things done even when your hands are full. Reply to a friend, set a reminder, or ask for directions. Just hold the power button or say “OK Google”.

Check out the some of Snapdragon Wear powered smartwatches that made a splash at this year’s Baselworld:

Apr 18, 2017

Snapdragon

Caffe2 and Snapdragon usher in the next chapter of mobile machine learning

Machine learning, at its core, is a method by which we can turn huge data into useful actions. Most of the attention around machine learning technology has involved super-fast data processing applications, server farms, and supercomputers. However far-flung servers don’t help when you’re looking to magically perfect a photo on your smartphone, or to translate a Chinese menu on the fly. Making machine learning mobile — putting it on the device itself — can help unlock everyday use cases for most people.

Qualcomm Technologies’ engineers have been working on the machine learning challenge for years, and the fruits of that work are evident in Qualcomm Snapdragon mobile platforms, which have become a leader for on-device mobile machine learning. It’s a core component of the Snapdragon product line, and you’ll see machine learning technologies both in our SoCs (820, 835, and some 600-tier chipsets) and adjacent platforms like the IoT and automotive.

And we aren’t pushing this technology forward by ourselves. We’re working with a whole ecosystem of tools, savvy OEMs, and software innovators to proliferate new experiences for consumers. These experiences use on-device machine learning, and we could not have conceived of them all by ourselves.

An exciting development in this field is Facebook’s stepped up investment in Caffe2, the evolution of the open source Caffe framework. At this year’s F8 conference, Facebook and Qualcomm Technologies announced a collaboration to support the optimization of Caffe2, Facebook’s open source deep learning framework, and the Qualcomm Snapdragon neural processing engine (NPE) framework. The NPE is designed to do the heavy lifting needed to run neural networks efficiently on Snapdragon, leaving developers with more time and resources to focus on creating their innovative user experiences. With Caffe2’s modern computation graph design, minimalist modularity, and flexibility to port to multiple platforms, developers can have greater flexibility to design a range of deep learning tasks including computer vision, natural language processing, augmented reality, and event prediction, among others.

Caffe2 is deployed at Facebook to help developers and researchers train machine learning models and deliver artificial intelligence (AI)-powered experiences in various mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.

One of the benefits of Snapdragon and the NPE is that a developer can target individual heterogeneous compute cores within Snapdragon for optimal performance, depending on the power and performance demands of their applications. The Snapdragon 835 is designed to deliver up to 5x better performance when processing Caffe2 workloads on our embedded Qualcomm Adreno 540 GPU (compared to CPU). The Hexagon Vector eXtensions (HVX) in the Qualcomm Hexagon DSP are also engineered to offer even greater performance and energy efficiency. The NPE includes runtime software, libraries, APIs, offline model conversion tools, debugging and benchmarking tools, sample code, and documentation. It is expected to be available later this summer to the broader developer community.

Qualcomm Technologies continues to support developers and customers with a variety of cognitive capabilities and deep learning tools alongside the Snapdragon platform. We anticipate that developers will be able to participate in a wider and more diverse ecosystem of powerful machine learning workloads, allowing more devices to operate with greater security and efficiency.

We don’t yet know the full range of applications for the technology, but we can’t wait to see how it’s used by innovative developers around the world.

Sign up to be notified when the Snapdragon neural processing engine SDK is available later this summer.

Apr 18, 2017

Snapdragon

Artificial intelligence tech in Snapdragon 835: personalized experiences created by machine learning

As our mobile devices have matured, gaining the ability to connect to the Web, we’ve labeled them as “smart.” But why settle for just smart? Harnessing the power of the Qualcomm Snapdragon 835 processor, developers, and OEMs are taking our devices to the next level, creating new experiences with the aid of machine learning. From superior video and security to your own personal assistant, your Snapdragon device has the ability to operate intelligently — outside of the cloud or Web connection — allowing you to experience your smarter phone in an entirely new way.

Application developers and device manufacturers understand what their users want. They can create a feature or an application that uses machine learning (more specifically, deep neural networks) to improve the performance a particular task, such as detecting or recognizing objects, filtering out background noise, or recognizing voices or languages. These applications are usually run in the cloud, and depending on the device they’re in, this could be sub-optimal.

The Snapdragon Neural Processing Engine SDK was created to help developers determine where to run their neural network-powered applications on the processor. For example, an audio/speech detection application might run on the Qualcomm Hexagon DSP and an object detection or style transfer application on the Qualcomm Adreno GPU. With the help of the SDK, developers have the flexibility to target the core of choice that best matches the power and performance profile of the intended user experience. The SDK supports convolutional neural networks, LSTMs (Long Short-Term Memory) expressed in Caffe and TensorFlow, as well as conversion tools designed to ensure optimal performance on Snapdragon heterogenous cores.

The Hexagon DSP and its wide vector extensions (HVX) offer an impressive power and performance mix for running neural networks on device. Performance is up to 8X faster and 25X more power efficient than using the CPU, which translates to lower battery consumption overall. In addition to support via the Snapdragon Neural Processing Engine, TensorFlow is directly supported on the Hexagon DSP, giving developers multiple options to run their chosen neural network power apps.

Here are a few applications that could be facilitated by Snapdragon 835 on-device machine learning tech:

Photography: Machine learning can aid in scene classification, real-time noise reduction, and object tracking, making it easier to take the perfect shot, or capture video regardless of the conditions.

VR/AR: With machine learning on your device, VR/AR feature can operate faster and with less lag, so everything from gestures and facial recognition to object tracking and depth perception are an immersive experience.

Voice detection: Your phone’s on-device AI can listen for commands and keywords to help you navigate the data and apps on your device more efficiently, and save power doing so.

Security: With facial recognition software and iris scanning, all operating independently from the cloud, your device can learn to identify, and help protect, you.

Connections: Your Snapdragon device has the ability to filter out distracting background noise during calls for clearer conversations with friends and family.

Qualcomm Technologies’ unique machine learning platform is engineered so devices powered by the Snapdragon 835 can run trained neural networks on your devices without relying on a connection to the cloud. Pretty innovative, right?

Take a look at our previous deep dives into each of the Snapdragon 835 key components — batteryimmersive AR and VRphotos and videoconnectivity, and security — all of which combine to make the Snapdragon 835 mobile platform truly groundbreaking.

And sign up to receive the latest Snapdragon news.

Apr 13, 2017

OnQ

Qualcomm Technologies leading the pack to help make 600 MHz mobile devices a reality

The FCC recently announced the end of bidding in the auction of the 600 MHz spectrum and that the results will be released soon. Qualcomm Technologies is ready for the much-awaited deployment of 600 MHz spectrum. We have been involved in the FCC proceedings from the beginning to ensure that the band plan was technically optimal and could be efficiently incorporated into products in a timely and cost effective manner.

This prime, low-band spectrum will bring greater capacity and improved coverage to mobile operators’ networks — important benefits for consumers — but it also comes with new antenna design challenges for OEMs, because it stretches the range of frequencies supported in mobile devices, such as smartphones, to new extremes at the low end of the radio spectrum.

The Qualcomm Snapdragon X20 LTE modem and RF transceiver have been designed with 600 MHz band capability. Our advanced RF Front End (RFFE) technologies, such as dynamic antenna tuning, are designed to minimize the OEM design impact in extending their devices’ frequency range to operate in the 600 MHz band without having to increase antenna size or compromise RF performance. In the evolution towards 5G, dynamic antenna tunability will be critical in accommodating the rapidly expanding frequency range of antennas in mobile devices while minimizing the impact on their form factors.

We are working closely with operators and OEMs to facilitate early launches of 600 MHz-capable 4G multimode/multiband devices, incorporating our industry-leading modem, transceiver, and RFFE technologies as part of our Snapdragon mobile platforms.

Apr 5, 2017
Developer

Heterogeneous Computing: An architecture and a technique

If you’re looking to create great mobile experiences, optimization isn’t optional: it’s a crucial step that helps transform good ideas into great execution. In our previous “Start Cooking with Heterogeneous Computing Tools on QDN” blog, we discussed the concept of heterogeneous computing and how it can help you get more from mobile hardware by sending computational tasks to the best suited processor. Heterogeneous computing is designed to help you achieve better application performance while improving thermal and power efficiency.

However, not all systems capable of heterogeneous computing are created equal and it’s important to understand why. Heterogeneous computing is both a computational technique and a hardware architecture. To achieve greater benefits, you are better served with hardware architected for heterogeneous computing from the ground up along with a software stack that facilitates heterogeneous computing techniques. It’s the combination of purpose-built hardware and a software stack offering granular control within a larger framework of system abstraction that allows for the deep optimizations that heterogeneous computing can deliver.

The Qualcomm Snapdragon Mobile Platform is designed on these principles. This starts with the microarchitecture – the choices made in platform circuitry that include how individual processors are engineered for high performance and how to optimize compute paths between the processors. Let’s look at the main components of the Snapdragon mobile platform and a few of the microarchitecture considerations that went into its design:

Qualcomm Kryo 280 CPU

Designed to handle complex workloads like web browsing and in-game artificial intelligence, the Kryo 280 features an octa-core processor with independent high efficiency and high performance core clusters. During normal operation, the high efficiency cores run most tasks while the high-performance cores activate for anything needing more power.

Qualcomm Hexagon 682 DSP

With the Hexagon wide Vector eXtensions (HVX), the Hexagon DSP excels at applications requiring heavy vector data processing, such as 6-DOF (or Degrees of Freedom) head motion tracking for virtual reality, image processing, and neural network computations. With a 1024-bit instruction word capability and dual execution of the control code processor and the computational code processor within the DSP, Hexagon can achieve breakthrough performance without draining system power.

Qualcomm Adreno 540 GPU

Ideal for arithmetic-heavy workloads that require substantial, parallel number crunching like 3D graphics rendering and camcorder image stabilization, the Adreno GPU is engineered to achieve improved power efficiency and 40% better performance than predecessors. Designed to deliver up to 25% faster graphics rendering and 60x more display colors compared to previous designs, the Adreno GPU supports real-life-quality visuals, and can perform stunning visual display feats like stitching together 4K 360 video in real time.

Heterogeneous computing in microarchitecture design

Beyond the performance enhancements among the individual processors, the Snapdragon mobile platform was designed to optimize the use of the processors together. For example, the Hexagon DSP can bypass DDR memory and the associated data shuffling CPU cycles by streaming data directly from sensors to the DSP cache. Similarly, the Adreno GPU supports 64-bit virtual addressing, allowing for shared virtual memory (SVM) and efficient co-processing with the Kryo CPU. These are just two of the microarchitecture design choices in the Snapdragon mobile platform that make it cutting-edge for heterogeneous computing.

Software

As we noted at the beginning of this post, heterogeneous computing is also a technique. And to truly support heterogeneous computing requires a software stack that provides developers the abstractions and the control to leverage the optimizations in the hardware per the requirements of their application.

To program the DSP or the GPU for heterogeneous computation, and to maximize their performance, developers can use the Qualcomm Hexagon SDK and the Qualcomm Adreno SDK, respectively. These SDKs open a toolbox of controls allowing for precision manipulation of data and computational resources.

For system-wide heterogeneous computing control, Qualcomm Symphony system manager SDK provides the software utilities designed to achieve better performance and lower power consumption from the Snapdragon mobile platform. Symphony is designed to manage the entire platform in different configurations so that the most efficient and effective combination of processors and specialized cores are chosen to get the job done as quickly as possible, with minimal power consumption.

On top of these SDKs it is possible for developers to build their applications directly – many developers opt for this route. However, there is a growing ecosystem of SDKs, frameworks and supporting libraries for accelerating development within a given application domain. Two examples of this are QDN's Adreno SDK for Vulkan for the Vulkan graphics API and our recently released Snapdragon VR SDK.

How to Put Heterogeneous Computing Techniques into Practice with Tools from Qualcomm Developer Network

Mar 23, 2017