DJI, the Chinese drone making behemoth has been added to the US government’s ‘entity list’ which lists companies considered a threat to national security. Firms on the list are effectively banned from trading with the US, meaning that, in effect, drones manufactured by SZ DJI Technology Co can no longer export components or software from US firms. It makes DJI the highest-profile addition to the list since Huawei, over two years ago.
DJI joins AGCU Scientech; China National Scientific Instruments and Materials and Kuang-Chi Group who Reuters reports are amongst ‘dozens’ of new additions who, “enabled wide-scale human rights abuses within China through abusive genetic collection and analysis or high-technology surveillance.” On the same conference call, the US Commerce Department also alleged that the firms had
Whilst not as big a household name, the other notable addition is China’s biggest chipmaker, SMIC, first rumored to be headed for the digital naughty-step in September. Because of its Chinese base, SMIC was seen as one of the last chances for Huawei to continue its HiSilicon SoC range, which has been scuppered by the lack of access to other ARM-based chips, as a result of being on the entity list. The HiSilicon 710A was manufactured by SMIC using some doddery old 14nm process chips, and now it seems that even that option is off the table, giving Huawei very few options for creating phones for export. That isn’t enough to kill it completely, as its a brand that still has huge customer loyalty at home, but it will do nothing to help its global ambitions – which, for the US, is precisely the point.
DJI is yet to comment on its own reclassification. We have no way of knowing how much existing stock of components the company has on hand, how long it will last, and how long it will take DJI to find an acceptable alternative. Being placed on the Entity List does not automatically mean a ban on the sale of a firm’s products, though it increases the likelihood that, as happened with Huawei, it could indicate it happening in the future.
Google is no stranger to a bandwagon or two, and today it’s hooking up with Disney+ to celebrate the Season 2 finale of its hit Star Wars spin-off ‘The Mandalorian’. Google recently celebrated the one billionth device running its ARCore framework. Put the two together, and what do you get? Your very own Grogu (alright, Baby Yoda, if you must), living in 3D augmented reality, wherever you are.
The collaboration is the latest example of Google’s 3D AR search results. If you search Google, with your compatible mobile device, for “Grogu”, “Baby Yoda” or “Mandalorian”, you’ll see a 3D model at the top of your search results. Click “view in 3D” and he’ll go full screen and animate. You can zoom in or out, or spin him around within the browser, or if you want to adopt him, there’s a button marked “View in your space”, which brings him out to wander around, wherever your camera is pointing, once you’ve told Google where the floor is.
You may be disappointed to find out that, once he gets there, Grogu doesn’t actually do a lot, apart from standing there, occasionally looking around and generally being a cute alien. There’s no interaction to speak of, whereas avatars from Google’s now erstwhile Playground feature were far more prone to react to their environment. However, he does make all those adorable little noises that have made him such a hit.
This is the second collaboration between Google and The Mandalorian, following on from The Mandalorian AR Experience, which launched last month, though only on a limited range of handsets.
The Grogu AR search result is the first tie-in we’ve seen, in a service that tends to concentrate on real animals, birds, biological functions and cultural or heritage sites. It does mean, however, that a few more people will learn about the fun you can have making AR objects appear in your space. As you can see, in this case, Grogu is getting to know the local wildlife.
Sometimes it takes Xiaomi a long time to release the kernel source code for a device they sell, but other times it can come rather hastily. While the Chinese OEM’s timeliness factor remains questionable, a kernel source release still excites the aftermarket development community, whenever it does land. And we are happy to report that a handful of new devices have had their kernel sources released, namely the Redmi 9, the Redmi 9 Power (sold as the Redmi Note 9 4G in China), and the Mi 10T Lite AKA the Redmi Note 9 Pro 5G.
Redmi 9 / Redmi 9 Prime
The Redmi 9 (code-name: “lancelot”) was released in June. The smartphone made its way to the Indian market as the Redmi 9 Prime back in August (not to be confused with the Indian Redmi 9). Xiaomi has now published kernel sources for the device based on the Android 10 release. The time gap is not ideal, but better late than never.
The newly released Redmi 9 Power’s kernel source code has been released as well, so developers can get to work on porting TWRP, creating custom AOSP-based ROMs, and custom kernels for the phone. If you need a brief refresher on the device, it’s based on the Redmi Note 9 4G China (code-name: “lime”), which is itself a close relative of the POCO M3 (code-name: “citrus”).
The China-exclusive Redmi Note 9 Pro 5G (code-name: “gauguinpro”) touts a few noteworthy features, such as the Qualcomm Snapdragon 750G SoC and a 6.67-inch, 120Hz LCD display panel. In fact, the device also shares a lot of the same specifications as the Mi 10T Lite (code-name: “gauguin”), which is why the kernel tree repo linked below is compatible with both of them.
Having access to kernel source code doesn’t necessarily mean the devices will have great custom development support, but given the immense popularity of Xiaomi’s budget and mid-range devices, there’s little doubt in our minds that all three phones won’t have access to a variety of custom ROMs in the near future.
The PlayStation 5 and Xbox X Series have made headlines this holiday season, and rightly so. However, unprecedented processing power comes with a hefty price tag.
If you are still waiting to upgrade, you might want to enter theUltimate Gaming Giveaway. One lucky winner will walk away with both consoles and a huge pile of gaming gear, worth $5,000. To enter, you simplydonate to charity.
As every gamer knows, the PS5 and Xbox X Series are the most powerful consoles ever made. The winner of this giveaway will enjoy titles like Watch Dogs: Legion, Gran Turismo 7, Cyberpunk 2077, Gear Tactics, and many more at smooth 4K resolution.
Of course, you need a decent TV for that — so the prize includes a Sony X900H 65” 4K Smart LED TV.
You also get a Secretlab TITAN Gaming Chair, a Bose QuietComfort 35 Series 2 Gaming Headset, and a Corsair Optical Gaming Keyboard. Plus, you get five years of online multiplayer with PlayStation Plus and Xbox Game Pass Ultimate membership.
For your chance to win this epic prize, you are asked to donate to the Playing For Change Foundation.
This non-profit organization uses music education to enrich the lives of kids around the world and provide jobs for talented musicians.
Samsung has posted a teaser video on social media today, confirming the launch of its much anticipated new Exynos chipset. Although not specified, this will be the Exynos 2100, the firm’s biggest silicon release in several years, and the beating heart of the upcoming Samsung Galaxy S21 series in most regions outside the US.
The chip is due to launch on the 12th January 2021, which by no coincidence is two days before the Samsung Galaxy S21 phone is revealed. The move shows some serious confidence from Samsung. After the poor reception of the Exynos 990, which powered the Samsung Galaxy S20 range, yet lagged behind the Qualcomm Snapdragon 865 in every respect, the South Korean firm will be looking to the S21 to not only preserve its reputation for mobile devices but simultaneously revive its reputation as a chip-maker.
If the rumored specs, spotted by our friends at WinFuture are to be believed (and they usually are), they may just get away with it. It’s said to be an octa-core with an ultra-high performance core clocking in at 2.9GHz, flanked by three more high-end cores at 2.8Ghz and four smaller cores running at 2.4GHz. It’ll include a 5G modem as standard. GPU comes from a Mali-G78 GPU. Samsung has a multi-year deal with AMD for GPUs, due to belatedly kick in this year, making it likely that this is one of the last flagships to run on Mali.
It’s not currently known whether the Exynos 2100 will be fabbed at 5nm, like the recently released Exynos 1080, or will stick to the more established 7nm format, which offers the company higher yields for an in-demand SoC. The key thing, however, is how the Exynos 2100 compares to the Qualcomm Snapdragon 888, which will power the device in North American markets. Samsung has always maintained that its chips are on par with Qualcomm’s, but user experience has repeatedly taught us otherwise. If the Exynos 2100 falls a long way short of the Snapdragon 888, it could be curtains for Samsung as a flagship silicon vendor.
Earlier this month, Qualcomm invited journalists to a virtual Snapdragon Tech Summit where they announced the Snapdragon 888 mobile platform. Qualcomm’s latest 8-series SoC brings major improvements to image processing and machine learning but only incremental improvements to CPU and GPU performance. To find out just how much more powerful Qualcomm’s latest chipset is, we typically get the opportunity to run benchmarks on its reference hardware. Due to COVID-19, however, Qualcomm could not arrange an in-person benchmarking session, so instead, they sent us a prerecorded video showing a Qualcomm Snapdragon 888 reference device running the gamut of popular benchmarks.
On the Snapdragon 888 reference device, Qualcomm ran one holistic benchmark (AnTuTu), a CPU-centric benchmark (Geekbench), a GPU-centric benchmark (GFXBench), and several AI/ML benchmarks (AIMark, AITuTu, MLPerf, and Procyon). Each benchmark was run three times, so the company shared the average result across three iterations. In addition, the company says they ran each benchmark using the default settings on the Snapdragon 888 reference design, meaning they did not enable any high-performance mode. However, because the benchmark scores were provided for us, we can’t verify the results or testing conditions for ourselves. Once we get our hands on a commercial device with the Qualcomm Snapdragon 888, we’ll rerun these benchmarks.
If you’re interested in reading up on all the specifications and features of the Qualcomm Snapdragon 888 mobile platform, then I recommend reading Idrees Patel’s excellent explainer on the Snapdragon 888 published earlier this month. His article goes into detail about all the improvements Qualcomm made to the CPU, GPU, modem, connectivity subsystem, ISP, AI engine, DSP, and everything else. For quick reference, I put together a chart comparing the key specifications of the Qualcomm Snapdragon 888 reference device compared to the other two devices used in this benchmark comparison: the Snapdragon 865-powered reference device and Snapdragon 855-powered Pixel 4 that I used in last year’s benchmarking session. You can find that chart below ahead of the benchmark results.
AnTuTu: This is a holistic benchmark. AnTuTu tests the CPU, GPU, and memory performance, while including both abstract tests and, as of late, relatable user experience simulations (for example, the subtest which involves scrolling through a ListView). The final score is weighted according to the designer’s considerations.
GeekBench: A CPU-centric test that uses several computational workloads including encryption, compression (text and images), rendering, physics simulations, computer vision, ray tracing, speech recognition, and convolutional neural network inference on images. The score breakdown gives specific metrics. The final score is weighted according to the designer’s considerations, placing a large emphasis on integer performance (65%), then float performance (30%) and finally crypto (5%).
GFXBench: Aims to simulate video game graphics rendering using the latest APIs. Lots of onscreen effects and high-quality textures. Newer tests use Vulkan while legacy tests use OpenGL ES 3.1. The outputs are frames during test and frames per second (the other number divided by the test length, essentially), instead of a weighted score.
Aztec Ruins: These tests are the most computationally heavy ones offered by GFXBench. Currently, top mobile chipsets cannot sustain 30 frames per second. Specifically, the test offers really high polygon count geometry, hardware tessellation, high-resolution textures, global illumination and plenty of shadow mapping, copious particle effects, as well as bloom and depth of field effects. Most of these techniques will stress the shader compute capabilities of the processor.
Manhattan ES 3.0/3.1: This test remains relevant given that modern games have already arrived at its proposed graphical fidelity and implement the same kinds of techniques. It features complex geometry employing multiple render targets, reflections (cubic maps), mesh rendering, many deferred lighting sources, as well as bloom and depth of field in a post-processing pass.
MLPerf Mobile: MLPerf Mobile is an open-source benchmark for testing mobile AI performance. It was created by MLCommons, a non-profit, open engineering consortium, to “deliver transparency and a level playing field for comparing ML systems, software, and solutions.” MLPerf Mobile’s first iteration provides an inference-performance benchmark for a handful of computer vision and natural language processing tasks. For more information, refer to the paper “MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It.”
Image classification: This test involves inferring a label to apply to an input image. Typical use cases include photo searches or text extraction. The reference model used is MobileNetEdgeTPU with 4M parameters, the dataset is ImageNet 2012 (224×224), and the quality target is 98% of FP32 (76.19% Top-1).
Image segmentation: This test involves partitioning an input image into labeled objects. Typical use cases include self-driving or remote sensing. The reference model used is DeepLab v3+ with 2M parameters, the dataset is ADE20K (512×512), and the quality target is 93% of FP32 (0.244 mAP).
Object detection: This test involves drawing bounding boxes around objects as well as providing a label for those objects. Typical use cases involve camera input such as for hazard detection or traffic analysis while driving. The reference model is SSD-MobileNet v2 with 17M parameters, the dataset is COCO 2017 (300×300), and the quality target is 97% of FP32 (54.8% mIoU).
Language processing: This test involves responding to questions colloquially. Typical use cases include online search engines. The reference model is MobileBERT with 25M parameters, the dataset is mini Squad (Stanford Question Answering Dataset) v1.1 dev, and the quality target is 93% of FP32 (93.98% F1).
AnTuTu Results
Starting off with AnTuTu, we can see that the Qualcomm Snapdragon 888 reference device scored nearly 17,000 points higher than the Snapdragon 865 reference device and nearly 350,000 points higher than the Snapdragon 855-powered Pixel 4. When you look at the CPU, GPU, Memory, and UX subscores (not shown here), we can see that the biggest improvements in performance come from GPU and memory. The Snapdragon 888 QRD scored approximately 45.56% higher in AnTuTu’s GPU subtest compared to the Snapdragon 865 QRD. Similarly, the Snapdragon 888 QRD scored about 52.08% higher in AnTuTu’s memory subtest compared to the Snapdragon 865 QRD. Compared to the Snapdragon 855-powered Pixel 4, the 888 QRD outscored it in the GPU and memory subtests by 98.42% and 117.58%, respectively.
Meanwhile, the Snapdragon 888 QRD scored approximately 30.05% and 90.28% higher in AnTuTu’s CPU subtest compared to the Snapdragon 865 QRD and Snapdragon 855-powered Pixel 4, respectively. The UX subscore is difficult to compare because of the different Android OS versions each device was running (the Pixel 4 and Snapdragon 865 QRD were running Android 10 when I benchmarked them last year, while the 888 QRD is running Android 11.)
The big boost in memory performance is quite interesting. Both the 865 QRD and the 888 QRD feature 12GB of LPDDR5 RAM, though we don’t know what the RAM is clocked at. Notably, the 865 supports up to 16GB of LPDDR5 RAM at 2750MHz, while the 888 supports up to 16GB of LPDDR5 RAM at 3200MHz. The bumps in CPU and GPU performance here are slightly above our expectations, as Qualcomm said the Snapdragon 888’s CPU and GPU gains are 25% and 35% respectively year-on-year. The more CPU and GPU centric benchmarks that follow show gains that are more in line with our expectations, though.
Geekbench Results
In Geekbench 5.0, the Qualcomm Snapdragon 888 performs 22.17% and 9.97% higher in the single-core and multi-core tests respectively compared to the Snapdragon 865. Compared to the Snapdragon 855, the 888 performs about 89.17% and 51.82% better respectively.
Qualcomm says the Snapdragon 888 provides a 25% boost in CPU performance over the Snapdragon 865. The CPU’s lone ARM Cortex-X1 Prime core is clocked at a conservative 2.84GHz — the same clock speed as the last-gen ARM Cortex-A77 Prime core — so it’s possible that we’ll see a 3+GHz clock speed for the inevitable mid-year Snapdragon 888 “Plus” refresh. If that’s the case, we can expect the CPU performance to improve even further, though right now, it’s fair to say the gains are solid, yet merely incremental.
Thus, if you’re upgrading from a two-year-old flagship, the 888 should bring major improvements in CPU performance. If you’re upgrading from a year-old flagship, those gains are much smaller. I’m personally excited to see how a Snapdragon 888 device handles console emulation.
GFXBench Results
Qualcomm hasn’t disclosed the core count or maximum frequency of the Adreno 660 GPU in the Snapdragon 888, so we have little to say about the GPU other than its gains in performance. In GFXBench’s Manhattan test, which uses the OpenGL ES 3.0 API and renders a 1080p scene offscreen, the Snapdragon 888 had an average framerate of 169fps, about 34.13% and 83.7% higher than the framerates achieved by the Snapdragon 865 and 855 respectively. In GFXBench’s Aztec Ruins test, which uses the Vulkan graphics API and renders a 1080p scene offscreen, the Snapdragon 888 had an average framerate of 86fps, about 38.71% and 95.45% higher than the framerates achieved by the Snapdragon 865 and 855 respectively.
There aren’t very many games that demand a lot of GPU horsepower (the recent Genshin Impact is one exception), but improved GPU performance is useful for more than just gaming. But, gaming is definitely the biggest reason why people will care about these benchmark results, and the Snapdragon 888 definitely delivers with its 35% faster graphics rendering and 20% better power efficiency year-on-year. These results only demonstrate the peak GPU performance, however, so we’ll have to revisit GFXBench—once we get our hands on commercial hardware—in order to run the benchmark’s long-term performance tests.
MLPerf Results
Perhaps the most interesting gains are in AI performance. Qualcomm generally makes huge leaps in AI performance each year, but this year’s gains are the most impressive. The Snapdragon 888’s AI engine boasts 26 TOPS performance, an increase from the 15 TOPS performance of the Snapdragon 865 and 7 TOPS performance of the Snapdragon 855. Qualcomm credits much of this gain to the new fused AI accelerator architecture of the Hexagon 780 DSP, fusing the scalar, vector, and tensor accelerators to eliminate physical distances and pool memory for sharing and moving data efficiently.
It is difficult for us to demonstrate just how significant this leap in performance actually is, however. We’ve talked in-depth about the difficulties of AI benchmarking during our interviews with Qualcomm’s Travis Lanier, Gary Brotman, and Ziad Asghar. The good news is that, since our discussions with Qualcomm execs, there have been significant advances in the field of AI benchmarks.
At the beginning of this article, we mentioned that Qualcomm ran 4 different AI benchmarks on the Snapdragon 888 reference device: AIMark, AITuTu, MLPerf, and UL’s Procyon. Perhaps the most promising of these benchmarks is MLPerf Mobile, which is a soon-to-be-released, open-source mobile AI benchmark backed by multiple SoC vendors, ML framework providers, and model producers. Its initial batch of mobile inferencing results is public, so we used those results to compare with the Snapdragon 888. The results only cover 3 devices: the MediaTek Dimensity 820-powered Xiaomi Redmi 10X 5G, the Qualcomm Snapdragon 865+-powered ASUS ROG Phone 3, and the Exynos 990-powered Samsung Galaxy Note 20 Ultra 5G. Qualcomm did not provide latency results — only throughput figures — so we did not plot the full results as submitted by the vendors for verification by MLCommons.
In these select computer vision and natural language processing inferencing benchmarks, we can see that the Qualcomm Snapdragon 888 reference device achieved the highest scores in all four tests. Of the 3 previous-generation chipsets, MediaTek’s Dimensity 820 outperformed the Snapdragon 865+ and Exynos 990 in object detection, while the Exynos 990 outperformed the Snapdragon 865+ and Dimensity 820 in NLP. Qualcomm’s Snapdragon 865+ was generally competitive, scoring on par with the Dimensity 820 in image segmentation and outperforming it in NLP. In these specific inferencing tests with these specific models and datasets, the Snapdragon 888 outperformed the 3 last-gen chipsets.
It will be interesting to see what applications and features developers and OEMs can create using the AI prowess of the Snapdragon 888. Computer vision will play an especially important role in the many AI-enhanced videography features we’ll likely see in 2021, while improved NLP performance can likewise affect video adjacent aspects like audio recording.
We should note, however, that the Snapdragon 888’s results are unverified by MLCommons since part of the organization’s verification process require that the device be commercially available (Qualcomm’s reference devices are not sold through a carrier or as an unlocked phone). Furthermore, the performance depends on what ML models, numerical formats, and ML frameworks are chosen, as well as what ML accelerators are available.
Conclusion
Qualcomm’s Snapdragon 888 once again brings incremental improvements to CPU and GPU performance, but massive improvements to image processing and AI. Not many people upgrading from a two-year-old device will notice the improvements in CPU and GPU (unless they plan on running emulators or playing games like Genshin Impact), but they will definitely notice the other advancements that have been made in mobile technology. Devices have higher refresh rate displays, more cameras with higher resolution image sensors, support for 5G connectivity, and much more these days. The massive gains in AI performance will go unnoticed by the average user, but the possibilities that have opened up with Qualcomm’s new chipset are exciting to ponder. Real-time AI video enhancements, multi-camera streams, and much more are on the horizon next year, and companies like Google continue to surprise with the features they release backed by training machine learning models.
Qualcomm isn’t the only company making improvements to its SoC lineup, though. Samsung’s upcoming Exynos 2100 for the Galaxy S21 is said to bring major performance improvements. There’s also Huawei’s new HiSilicon Kirin 9000 and MediaTek’s growing Dimensity line of mobile SoCs. I hope to revisit these benchmarks once we have at least one top-of-the-line device with Samsung’s, Huawei’s, and MediaTek’s next-gen silicon.
Qualcomm Snapdragon 888 Benchmarking Demo
I mentioned at the beginning of this article that Qualcomm shared a prerecorded video with us. If you’re interested, I have uploaded that video to YouTube. It shows the Snapdragon 888 running all the benchmarks I shared above, as well as the remaining AI benchmarks that I didn’t showcase.
Meanwhile, here’s the table that Qualcomm provided us summarizing the Snapdragon 888’s benchmark results:
Benchmark results from a Qualcomm Snapdragon 888 reference device. Source: Qualcomm
Amazon has announced it is bringing a new feature to Alexa, just in time for this strangest of Christmases. The company has confirmed that its Group Calling feature, first trailed in September, is rolling out. It allows you to create a group (for example) ‘the family’ and start a group call in either audio or video, with a single command (ie ‘Alexa, call the family’).
Amazon first trailed the feature earlier in the year, when it said “Video calling and Drop In have been more popular this year than ever before. Soon, it will be easy for up to eight friends or family members to join a hands-free audio or video call with Alexa. Simply say, “Alexa, call my family,” for example, to connect with your family for the next catch-up, happy hour, or birthday celebration. You will be able to create and name groups in the Alexa app.“
As we approach our COVID-Christmas, which will see families separated in ways rarely seen in peacetime, tech companies are rushing to enable services to bridge the gap. Amazon said in the Fall that it would also be bringing visual effects to video calls, but it seems this has been delayed, to ensure the basic function was ready for Christmas. As it is, the group calls feature is said to work on ‘compatible devices’. Amazon lists its own-branded Echo series but doesn’t specify any third-party devices. We’re on the case and will add any we discover to this article.
This native support doesn’t affect services from other video calling services. At the moment, Amazon Alexa is the last of the major smart displays not to support direct calling via Zoom, which was promised to roll out across Alexa, Google Home and Facebook Portal in time for Christmas. There’s a week to go, it could still come, but they’re cutting it pretty fine. Hopefully, today’s announcement will serve as an effective alternative, for Christmas and the year ahead.
