At Hot Chips 2019, Intel revealed new details of upcoming high-performance artificial intelligence (AI) accelerators: Intel Nervana neural network processors, with the NNP-T for training and the NNP-I for inference. Intel engineers also presented technical details on hybrid chip packaging technology, Intel Optane DC persistent memory and chiplet technology for optical I/O. Myriad X is the first VPU to feature the Neural Compute Engine - a dedicated hardware accelerator for running on-device deep neural network applications. Interfacing directly with other key components via the intelligent memory fabric, the Neural Compute Engine is able to deliver industry leading performance per Watt without encountering common data flow bottlenecks encountered by other architectures. Qualcomm Technologies, Inc., a subsidiary of Qualcomm Incorporated (NASDAQ: QCOM), announced that it is bringing the Company's artificial intelligence (AI) expertise to the cloud with the Qualcomm Cloud AI 100. Built from the ground up to meet the explosive demand for AI inference processing in the cloud, the Qualcomm Cloud AI 100 utilizes the Company's heritage in advanced signal processing and power efficiency. Our 4th generation on-device AI engine is the ultimate personal assistant for camera, voice, XR and gaming – delivering smarter, faster and more secure experiences. Utilizing all cores, it packs 3 times the power of its predecessor for stellar on-device AI capabilities. With the open-source release of NVDLA's optimizing compiler on GitHub, system architects and software teams now have a starting point with the complete source for the world's first fully open software and hardware inference platform. The next generation of NVIDIA's GPU designs, Turing will be incorporating a number of new features and is rolling out this year. Nvidia launched its second-generation DGX system in March. In order to build the 2 petaflops half-precision DGX-2, Nvidia had to first design and build a new NVLink 2.0 switch chip, named NVSwitch.
Embedded AI can transform a tabletop speaker into a personal assistant; give a robot brains and dexterity; and turn a smartphone into a smart camera, music player, or game console. Traditional processors, however, lack the computational power to support many of these intelligent features. Chipmakers, startups, and capital are taking this opportunity to the market. According to a Gartner report, the chip market's total revenue hit US$400 billion in 2017, and the figure is expected to exceed US$459 billion in 2018. Traditional chip makers are putting an increasing focus on AI chip development, venture capital is pumping significant investments into the market, and AI chip startups are emerging.
Though machine learning has been around for more than three decades, it took a lot of time for the hardware to catch up with the demands of these power-hungry algorithms. With each passing year, the chip-set manufacturers have tried to make the hardware lighter and faster. Today, over 100 companies are working on building next-generation chips and hardware architectures that would match the capabilities of algorithms. These chips are capable of enabling deep learning applications on smartphones and other edge computing devices. Intel recently revealed new details of upcoming high-performance artificial intelligence accelerators: Intel Nervana neural network processors.
In my previous post on the recent Linley Processor Conference, I wrote about the ways that semiconductor companies are developing heterogeneous systems to reach higher levels of performance and efficiency than with traditional hardware. One of the areas where this is most urgently needed is vision processing, a challenge that got a lot of attention at this year's conference.
The Internet of Things (IoT) has sparked the proliferation of connected devices. These devices, which house sensors to collect data of the day-to-day activities or monitoring purposes, are embedded with microcontrollers and microprocessors chips. These chips are mounted based on the data sensor needed to complete an assigned task. So we don't have a one processor fits all architecture. For example, some devices will perform a limited amount of processing on data sets such as temperature, humidity, pressure, or gravity; more complicated systems, however, will need to handle (multiple) high-resolution sound or video streams.