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Top 10 Processors CPU and <span style='color:red'>GPU</span> Chip Companies in China

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Top 10 Processors CPU and GPU Chip Companies in China

　　China is home to some of the leading processor chip manufacturers in the world. These companies are responsible for producing some of the most advanced and innovative CPU, GPU, FPGA, and ASIC chips available on the market. In this article, we will be discussing the top 10 processor chip companies in China. Let’s get started!　　1、Unigroup Guoxin Microelectronics Co., Ltd.　　Unigroup Guoxin Microelectronics Co., Ltd. focuses on the design and development of integrated circuits chips. It is a leading provider of integrated circuit chip products and solutions. Its products and applications are all over the world. Its core business areas such as chips, safe independent FPGA, power semiconductor devices, and ultra-stable crystal frequency devices have formed a leading competitive situation and market position.　　2、Hygon information Technology Co., Ltd.　　Hygon information Technology Co., Ltd. was established in 2014. It is mainly engaged in the research and development of computing chip products and systems such as high-end processors and accelerators. It aims to become a world-class chip company and provide core computing engines for Digital China.　　3、Amlogic(Shanghai) Co., Ltd.　　Amlogic is a world leading fabless semiconductor company that specializes in the design, development and application of high-performance, multimedia system-on-chip (SoC). As a result of its cutting-edge technologies and best-in-class solutions, it has actively expanded into new areas including smart vision, wireless connectivity and automotive electronics, ushering in a new era of smart life.　　By providing complete turnkey solutions in combination with industry-leading software and hardware technologies, including UHD multimedia processing, content security protection, advanced CPU and GPU, customers can rapidly optimize and develop market-leading products with state-of-the-art performance and power consumption.　　4、Hunan Goke Microelectronics Co., Ltd.　　Established in 2008, Goke Microelectronics Co., Ltd. has its headquarters in Changsha and subsidiary offices in Chengdu, Shanghai, Shenzhen, Beijing and Changzhou.GOKE spends more than 20% of its annual revenue on research and development.　　GOKE is committed to developing mass-volume integrated circuit solutions for storage,smart set-top boxes and IOT. It has introduced a series of products with in-house IP. These include ASICs and SOCs that incorporate the NDS advanced security decoder, the H.265 high-definition codec, high-performance acoustic processor, enterprise solid-state drive controller.　　5、Shanghai Fullhan Microelectronics Co., Ltd.　　Fullhan Microelectronics was established in April 2004 and focus on video semiconductor design.The mission of Fullhan is providing the most competitive chips and solutions for video products.　　Fullhan provides H.264 codec SoC,image signal processor IC and various solutions of video products.　　Fullhan works closely with solution providers and equipment manufacturers from domestic and worldwide to provide high performance, cost-effective products and services, increase market shares and achieve substantial business success.　　6、Shanghai Anlogic Infotech Co., Ltd.　　Shanghai Anlogic Infotech Co., Ltd. founded in November 2011, is a leading integrated circuit design company in China.　　The company has the independent research and development capabilities of FPGA chip hardware and FPGA compilation software, focusing on the development of general programmable logic chip technology and system solutions. The SALPHOENIX® high-performance product series, SALEAGLE® high-efficiency product series, SALELF® low-power product series and SALSWIFT® system chip series FPSoC® produced by the company have been successfully used in industrial control, consumer electronics, medical equipment, and networks with excellent quality. communications and other fields.　　7、Rockchip Electronic Co., Ltd.　　Rockchip Electronics Co., Ltd., established in 2001, is headquartered in Fuzhou, with branches and subsidiaries in Shenzhen, Shanghai, Beijing, Hangzhou, and Hong Kong. Rockchip is a leading fabless IC design company that focuses on Intelligent IoT.　　Rockchip specializes in SoC design, analog circuit chip design, and algorithm research. It has extensive experience in processor and analog-digital mixed chip design, multimedia processing, image algorithm. Rockchip has expertise in system software development. In addition to processor chips, it also provides full solutions including PMICs, analog-digital mixed chips, optoelectronic products, and development boards.　　8、Allwinner Technology Co., Ltd.　　Allwinner Technology, founded in 2007, is an outstanding designer dedicated to intelligent application SoC, high performance analog component and wireless connectivity IC. It is headquartered in Zhuhai China, with other R&D centers and offices in Shenzhen, Xi’an,Shanghai,Chengdu,Hengqin,Guangzhou,Beijing and HongKong,.　　Motivated by customer-oriented strategy, Allwinner aligns remarkable R&D teams with long-term core-technology investment in UHD video processing, high-performance multi-core CPU/GPU integration with AI and advanced manufacturing process in terms of high integration , ultra-low power consumption and full-stack integration platform, providing competitive turnkey solutions with considerate services. The products powered by Allwinner spread across from industry control, smart home, smart hardware, tablet, automotive electronics, robot, virtual reality, OTT box, wireless communication to analog products.　　9、Changsha Jingjia Microelectronics Co., Ltd.　　Changsha Jingjia Microelectronics Co., Ltd. was established in April 2006. It is committed to technology and comprehensive applications in the field of information detection, processing and transmission, and provides customers with highly reliable and high-quality solutions, products and supporting services.　　The company’s products cover integrated circuit design, graphics and image processing, computing and storage products, small radar systems, wireless communication systems, electromagnetic spectrum application systems, etc., and are widely used in professional fields such as aviation, aerospace, navigation, and vehicles that require high reliability.　　10、Loongson Technology Corparation　　In response to the demands for information development, Loongson Technology always stays abreast of international IT trends and focuses on industry development and system construction based on independent innovation. Until now, it has mastered core computing technologies on CPU instruction set architecture (ISA) design (LoongArch®), processor IP core, and operating system.　　On this basis, Loongson Technology strives to create an independent and open software and hardware ecosystem and information industry system so as to provide self-developed, safe, and reliable processors to meet national strategic needs as well as high-performance, low-cost processors and basic software and hardware solutions to boost the innovative development of the information industry.

Key word：

CPU

Release time：2023-10-19 14:23 reading：1516 Continue reading>>

Arm <span style='color:red'>GPU</span> Gets More AI Muscle

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Arm GPU Gets More AI Muscle

　　ARM announced four new cores for mainstream smartphones and digital TVs, two Mali GPUs and associated video and display cores for them. The news shows that Arm is, at least for now, taking a three-tier approach to machine learning and that China mobile OEMs are becoming increasingly influential.　　Arm’s new Mali G52 GPU core is aimed at mid-tier smartphones and digital TVs using combinations of Cortex-A72 and -A55 CPU cores. The GPU boosts machine-learning performance up to 3.6x for ImageNet classifiers compared to its existing G51 core.　　The G52 packs eight execution engines compared to four on the G51, with four lanes in each engine and each capable of up to four 8-bit integer multiply-accumulate operations per cycle. Up to four G52s can be used in an SoC, each executing up to 288 MACs/cycle.　　For the low end, a new G31 core uses Arm’s Bifrost architecture and targets systems using A55 CPUs. It is Arm’s smallest core to date to support the latest OpenGL ES and Vulkan graphics APIs but provides no specific acceleration for neural nets.　　The company previously announced that it is preparing dedicated neural-network acceleration cores for premium mobile systems as part of its Project Trillium.　　“We may not always have a dedicated machine-learning processor in these devices,” said an Arm spokesman.　　New display and video cores are targeted for use with the G52/31. The D51 display core aims to handle more jobs with significantly fewer accesses needed to external memory. The V61 video core supports 4K resolution at 60 frames/second as well as HDR10 rendering.　　Arm provided no results of third-party benchmarks for the cores.　　As of this year, more than a billion smartphones from China’s largest OEMs will be in use, with users outside of China doubling each year. China’s handset makers grew their share of the global pie to 945 million phones, 31% of total handset sales last year, according to stats that Arm showed from market watcher Newzoo.　　For its part, Arm said that 159 licensees have shipped a total of 1.2 billion Mali GPU cores to date. The cores are currently used in half of all handsets and 80% of digital TVs, it said.　　Arm’s Mali leads in the mobile GPU space with a 48% share with design wins in handsets, tablets, and TVs as well as some IoT and automotive systems, according to Jon Peddie Research. Qualcomm’s Snapdragon with its Adreno GPUs follows at 25%, and Imagination Technologies, which used to lead the sector, now is in third at 12%.

Key word：

AI

Release time：2018-03-09 00:00 reading：1771 Continue reading>>

Imagination Improves Virtualized <span style='color:red'>GPU</span> Core to Support Multiple Screens

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Imagination Improves Virtualized GPU Core to Support Multiple Screens

　　Imagination Technologies announced a new GPU core that supports hardware virtualization specifically for the automotive environment. The new PowerVR Series8XT GT8540 GPU, which can simultaneously drive up to six 4K screens with complex UIs at 60fps, supports the multiple, ultra-high resolution displays for cluster, head-up display (HUD) and infotainment that automotive manufactures are fitting to car interiors from 2018 onwards.　　The company says it already has licensees for the core, though multiple 4k screens are probably between three and five years away.　　"This might be a long way away, but silicon is being designed for these today," said Kristof Beets, Imagination's senior director of product management.　　"A lot of GPUs are focused on the PC or mobile phone, and then they are re-badged for automotive," Beets said. "With this new core, we have developed it especially for the automotive environment, based on our proven solution for automotive.”　　Beets said Imagination has been developing its virtualized solution for automotive over a number of years, so it is a proven market solution.　　The new four-cluster PowerVR Series8XT features built-in hardware virtualization capability, enabling automotive OEMs to deliver secure, high-performance graphics capabilities for a vehicle’s many displays. PowerVR hardware virtualization can provide a complete separation of services and applications, ensuring they remain secure against system intrusion or data corruption. The platform can support up to eight applications or services running in separate containers at once and automotive OEMs can deploy and remove services at will without affecting others running at the same time.　　Beets said the focus on virtualization is a key differentiator for Imagination's new core, with dynamic advance scheduling, flexible budgeting of resource for each VM (virtual machine) on the core, prioritization flexibility (for example guaranteeing the dashboard display), with near zero-impact on performance.　　As more in-car infotainment systems move towards richer operating systems, such as Android which offer capabilities to run apps, the importance of a GPU capable of full hardware virtualization to contain rogue apps is of increasing importance. Allowing a rich graphical environment for the infotainment system, safe in the knowledge that the dashboard and other critical displays will be unaffected by malware with a single PowerVR GPU, enables higher levels of system integration, reducing costs, whilst maintaining the safety critical requirements of the design.　　With enhanced design flexibility over the previous generation, the PowerVR four-cluster Series8XT enables automotive OEMs to design their systems to prioritise either graphics or compute-based applications. The Series8XT GT8540 can support long-running compute workloads on a single Shader Processing Unit (SPU) for ADAS functions such as lane departure warning, blind-spot detection, and surround view, amongst others. Other tasks, such as infotainment and cluster, can run on the second SPU, using prioritising mechanisms to reach system performance targets.　　Dominique Bonte, a vice president and managing director at ABI Research, said the ability to run multiple workloads over each SPU is a game changer in the automotive industry.　　"As the automotive industry moves closer to introducing Level 4 and Level 5 autonomous vehicles, powerful GPUs will be vital to power multiple high-resolution display," Bonte said. "With distraction concerns removed in driverless cars, additional opportunities for advanced infotainment features including 5G-based 4K video streaming will emerge in the longer term.

Key word：

Imagination, Virtualized GPU

Release time：2018-01-31 00:00 reading：2644 Continue reading>>

AMD Updates <span style='color:red'>GPU</span>, CPU Road Maps

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AMD Updates GPU, CPU Road Maps

　　AMD updated its x86 and graphics roadmaps at CES, giving first details of plans for 12-nm CPUs and a 7-nm GPU. It also rolled out six x86 chips with integrated graphics targeting a variety of desktop and notebook sockets.　　The company is now sampling a 12-nm upgrade of its Ryzen desktop processors launched last year in a 14-nm process. The chips will be in production in April using the upgraded process that Globalfoundries disclosed last year. Meanwhile, a Ryzen 2 design has been completed, but AMD gave no details of its internals or shipping dates.　　AMD’s first 7-nm GPU will be a version of its Vega design targeting machine learning. The company is playing catch-up with GPU rival Nvidia, which dominates the emerging area after rolling out Volta in 2017, its first chips with dedicated hardware for machine learning.　　The 7-nm GPUs will presumably be made at TSMC, AMD’s usual foundry for GPUs; however, the company did not specify its plans. Separately, AMD said that it will roll out a version of Vega for ultrathin notebooks this year.　　AMD expects to be in production in mid-February with its first two desktop processors that embed its Vega GPU cores as well as four of its x86 Zen cores. The chips run at 3.7 to 3.9 GHz, consume 45 to 65 W, and aim to enable gaming at 1080-progressive resolution without a discrete graphics card.　　Separately, AMD expects to be in production by June with a line of three high-end notebook processors also combining Vega GPU cores with four x86 Zen cores. They will run at up to 3.4 to 3.8 GHz and consume 15 W. AMD said that they will provide 25% to 50% more graphics performance than Intel’s i7-7500U and -8550U chips.　　Finally, AMD added one low-end product to its existing line of mainstream notebook chips that combine Zen and Vega cores.　　Shipping the integrated CPU/GPU products that AMD calls APUs is key for the company, said Nathan Brookwood, principal of market watcher Insight 64. Most of the Ryzen products that shipped in 2017 were x86-only chips that required systems to use discrete graphics cards.　　Notebooks are the biggest chunk of the PC market, and almost all of them use integrated graphics, and desktops with integrated graphics are the next biggest chunk. So AMD was constrained in the market that it could serve last year, and this year, their reach will be much greater, said Brookwood.　　The company might have a small advantage over rival Intel after the security issues raised last week.　　“AMD is less impacted by Meltdown than Intel,” he added. “I don’t think that’s a big deal for consumers, but some of them might be scared by it and prefer AMD parts.”

Key word：

AMD,GPU,CPU

Release time：2018-01-11 00:00 reading：1413 Continue reading>>

Algorithm Speeds <span style='color:red'>GPU</span>-based AI Training 10x on Big Data Sets

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Algorithm Speeds GPU-based AI Training 10x on Big Data Sets

　　IBM Zurich researchers have developed a generic artificial-intelligence preprocessing building block for accelerating Big Data machine learning algorithms by at least 10 times over existing methods. The approach, which IBM presented Monday (Dec. 4) at the Neural Information Processing Systems conference (NIPS 2017) in Long Beach, Calif., uses mathematical duality to cherry-pick the items in a Big Data stream that will make a difference, ignoring the rest.　　“Our motivation was how to use hardware accelerators, such as GPUs [graphic processing units] and FPGAs [field-programmable gate arrays], when they do not have enough memory to hold all the data points” for Big Data machine learning, IBM Zurich collaborator Celestine Dünner, co-inventor of the algorithm, told EE Times in advance of the announcement.　　“To the best of our knowledge, we are first to have generic solution with a 10x speedup,” said co-inventor Thomas Parnell, an IBM Zurich mathematician. “Specifically, for traditional, linear machine learning models — which are widely used for data sets that are too big for neural networks to train on — we have implemented the techniques on the best reference schemes and demonstrated a minimum of a 10x speedup.”　　IBM Zurich researcher Martin Jaggi at ?cole Polytechnique Fédérale de Lausanne (EPFL), also contributed to the machine learning preprocessing algorithm.　　For their initial demonstration, the researchers used a single Nvidia Quadro M4000 GPU with 8 gigabytes of memory training on a 30-Gbyte data set of 40,000 photos using a support vector machine (SVM) algorithm that resolves the images into classes for recognition. The SVM algorithm also creates a geometric interpretation of the model learned (unlike neural networks, which cannot justify their conclusions). IBM’s data preprocessing method enabled the algorithm to run in less than a one minute, a tenfold speedup over existing methods using limited-memory training.　　The key to the technique is preprocessing each data point to see if it is the mathematical dual of a point already processed. If it is, then the algorithm just skips it, a process that becomes increasingly frequent as the data set is processed. “We calculate the importance of each data point before it is processed by measuring how big the duality gap is,” Dünner said.　　“If you can fit your problem in the memory space of the accelerator, then running in-memory will achieve even better results,” Parnell told EE Times. “So our results apply only to Big Data problems. Not only will it speed up execution time by 10 times or more, but if you are running in the cloud, you won’t have to pay as much.”　　As Big Data sets grow, such time- and money-saving preprocessing algorithms will become increasingly important, according to IBM. To show that its duality-based algorithm works with arbitrarily large data sets, the company showed an eight-GPU version at NIPS that handles a billion examples of click-through data for web ads.　　The researchers are developing the algorithm further for deployment in IBM’s Cloud. It will be recommended for Big Data sets involving social media, online marketing, targeted advertising, finding patterns in telecom data, and fraud detection.　　For details, read Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems, by Dünner, Parnell, and Jaggi.

Key word：

Algorithm,GPU.AI ,Big Data Sets

Release time：2017-12-06 00:00 reading：1098 Continue reading>>

model	brand	Quote
BD71847AMWV-E2	ROHM Semiconductor
RB751G-40T2R	ROHM Semiconductor
TL431ACLPR	Texas Instruments
CDZVT2R20B	ROHM Semiconductor
MC33074DR2G	onsemi

model	brand	To snap up
STM32F429IGT6	STMicroelectronics
BP3621	ROHM Semiconductor
BU33JA2MNVX-CTL	ROHM Semiconductor
TPS63050YFFR	Texas Instruments
ESR03EZPJ151	ROHM Semiconductor
IPZ40N04S5L4R8ATMA1	Infineon Technologies

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