Despite everything we've all been through, though, there were a few bright spots in the world of tech. Console makers blessed us with mouthwatering next-gen hardware, while Apple wowed the industry with the prowess of its own M1 CPU. Google also delivered an excellent phone for just $350, demonstrating an ability to not just read the room, but also to think of a world beyond a well-heeled tech-savvy audience. There are also companies that flourished during the global lockdown, and though truth continued to be contested throughout the US elections, we thankfully saw social media step up their efforts to combat misinformation. Clearly, staying home gave some of us the freedom to produce great products and fight for good. Apple's M1 system on a chip (SOC) may be tiny, but its impact on the computing industry will be felt for years to come. The first of Apple's silicon to reach Macs, the M1 is a powerhouse, with 8 CPU cores and up to 8 GPU cores. Both the M1-equipped MacBook Air and MacBook Pro blew away comparable Intel or AMD-based PCs in the Geekbench 5 benchmark.
The AI chips increasingly focus on implementing neural computing at low power and cost. The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. Increasingly, the generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilities. Such a requirement to transit from application-specific to general intelligence AI chip must consider several factors. This paper provides an overview of this cross-disciplinary field of study, elaborating on the generalisation of intelligence as understood in building artificial general intelligence (AGI) systems. This work presents a listing of emerging AI chip technologies, classification of edge AI implementations, and the funnel design flow for AGI chip development. Finally, the design consideration required for building an AGI chip is listed along with the methods for testing and validating it.
Samsung Electronics' in-house incubation program C-Lab has been nurturing the innovative ideas of Samsung employees and helping bring them to fruition since 2012. The initiative is divided into C-Lab Inside and C-Lab Outside, with this year's "Inside" projects focused on encouraging healthier and more convenient lifestyles. A total of nine C-Lab teams will exhibit their work at CES 2020 in Las Vegas from January 7 to 10. While showcasing their work the teams will meet with future users from all over the world to discuss their ideas and try to determine how their innovations will be received. The C-Lab categories will be introduced in two installments, with Part 1 highlighting the five teams who were selected by the internal company venture program.
In this paper, we introduce the concept of intermittent learning, which enables energy harvested computing platforms to execute certain classes of machine learning tasks. We identify unique challenges to intermittent learning relating to the data and application semantics of machine learning tasks. To address these challenges, we devise an algorithm that determines a sequence of actions to achieve the desired learning objective under tight energy constraints. We further increase the energy efficiency of the system by proposing three heuristics that help an intermittent learner decide whether to learn or discard training examples at run-time. In order to provide a probabilistic bound on the completion of a learning task, we perform an energy event-based analysis that helps us analyze intermittent learning systems where the uncertainty lies in both energy and training example generation. We implement and evaluate three intermittent learning applications that learn the air quality, human presence, and vibration using solar, RF, and kinetic energy harvesters, respectively. We demonstrate that the proposed framework improves the energy efficiency of a learner by up to 100% and cuts down the number of learning examples by up to 50% when compared to state-of-the-art intermittent computing systems without our framework.
Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an alternative for predicting upcoming machine failures based on relevant data points. In this paper, we describe three different machine learning tasks that can be used for that purpose: (i) predicting Time-To-Failure (TTF), (ii) predicting health state, and (iii) predicting TTF intervals of an equipment. Our results show that trained machine learning models can outperform benchmarks resembling human judgments in all three tasks. This suggests that machine learning offers a viable alternative to currently deployed plasma etching equipment maintenance strategies and decision making processes.
If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. CES is the biggest technology show of the year, and every year Reviewed's crack team of product experts spend days sorting through the thousands of new releases that debut in Las Vegas. To weed out the pretenders and highlight only the things we think will actually make a splash in 2019. We call them our CES Editors' Choice winners, and once again we've found some truly exceptional products. Though there are plenty of flashy products making big promises, we focus on the stuff you're actually going to buy this year. All 40 of our winners strike a balance in our four key criteria: innovation, technology, design, and value. Congratulations to all of our winners and be sure to check back as we update this page with coverage of all of our winners as CES 2019 continues. Okay, while a "roll-up" OLED TV did debut last year, the LG R9--technically, the OLED65R9PUA--is a real, flesh-and-blood product.
Samsung is making a big move into robotics. At the Consumer Electronics Show on Monday, the Korean tech giant revealed a new lineup of helpful droids that can keep track of your health and get rid of harmful air in your home, among other things. Samsung also talked up a new shape-shifting TV, advancements in its Bixby AI assistant and smart home appliances that are expected to roll out soon. The Samsung Bot Care gets around on a set of wheels and has expressive eyes that blink and respond when it sees you. Samsung hopes the shiny white robot can help the elderly stay healthy by disbursing medication, checking vitals like blood pressure and heart rate, as well as keeping track of sleep quality.
Abstract--In this paper, a spintronic neuromorphic reconfigurable Array(SNRA) is developed to fuse together power-efficient probabilistic and infield programmable deterministic computing during both training and evaluation phases of restricted Boltzmann machines(RBMs). First, probabilistic spin logic devices are used to develop an RBM realization which is adapted to construct deep belief networks (DBNs) having one to three hidden layers of size 10 to 800 neurons each. The functionality of our proposed CD hardware implementation is validated using ModelSim simulations. We synthesize the developed Verilog HDL implementation of our proposed test/train control circuitry for various DBN topologies where the maximal RBM dimensions yield resource utilization ranging from 51 to 2,421 lookup tables (LUTs). Next, we leverage spin Hall effect (SHE)-magnetic tunnel junction (MTJ) based nonvolatile LUTs circuits as an alternative for static random access memory (SRAM)-based LUTs storing the deterministic logic configuration to form a reconfigurable fabric. Finally, we compare the performance of our proposed SNRA with SRAMbased configurablefabrics focusing on the area and power consumption induced by the LUTs used to implement both CD and evaluation modes. The results obtained indicate more than 80% reduction in combined dynamic and static power dissipation, while achieving at least 50% reduction in device count.
With the start of a brand new week, we're rounding up the best deals from Amazon, Walmart, Udemy, and Target on the products that will seriously upgrade your life. For the kitchen, there are a lot of deals on KitchenAid products, such as KitchenAid Artisan Series Tilt-Head Stand Mixer, which is priced at $209.99. This sale price is $290 off its retail price. In addition, the KitchenAid Professional Bowl-Lift Stand Mixer is on sale for $239.00. At Walmart, the Apple iPad Air 2 is discounted with a sale price $379.99,
LAS VEGAS, NV – MARCH 8, 2018 – Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, and Samsung Electronics Co. Ltd. today announced plans for a strategic partnership to connect Samsung's ARTIK Smart IoT Platform to the Philips HealthSuite Digital Platform. This collaboration will ultimately allow the Samsung ARTIK ecosystem of connected devices to safely access and share information with Philips' cloud platform. Healthcare application developers will be able to realize interoperable connected health solutions using integrated data sets and innovative HealthSuite services such as advanced health analytics. "This collaboration will enable healthcare application developers to focus on the development of innovative applications rather than on the technical integration of devices," said Dale Wiggins, General Manager Philips HealthSuite Digital Platform at Philips. "By strengthening our HealthSuite ecosystem with Samsung ARTIK, we will be taking another important step in breaking down the silos in today's healthcare domain to create a trusted and seamless care experience for both consumers and care professionals."