Qualcomm just announced the Snapdragon X2 Elite and X2 Elite Extreme, anticipated sequels to its game-changing PC chips. Qualcomm has announced its next generation of PC processors: the Snapdragon X2. It might not sound very exciting, but these chips continue to bring Windows laptops up to par with Apple Silicon-powered MacBooks . The company took the PC world by storm last year with its Snapdragon X chips, breaking the long-held duopoly of Intel and AMD powering most Windows laptops. Nearly every major laptop manufacturer was on board, launching a bevy of Qualcomm-powered laptops.

In 2022-2023, AI-powered PCs made quite a splash with their automatic generation and built-in virtual assistants. Those features are cool, sure, but they're a little gimmicky at first blush. That said, amid the hype, the real standout feature emerged: battery life. Thanks to smarter resource management and power-efficient chip architecture, AI PCs became long-lasting devices that didn't need to be plugged in all the time. Let's take flying cross-country with a traditional laptop, for instance.

Intel's second-generation Xe2 Arc GPUs are real, and once again, they could be compelling options for gamers looking for capable video cards under 250. Confirming leaks from the past week, Intel today unveiled the 249 Arc B580 and the slightly less capable 219 B570, both of which target 1,440p gaming. They feature the company's new XeSS2 AI capabilities (which are also coming to the older Arc cards), including Super Resolution upscaling (like the original XeSS), frame generation and low latency modes. The goal, according to Intel, is to deliver more performance per dollar compared to NVIDIA's 299 RTX 4060 and AMD's Radeon 7600. It's a noble pitch, but one that's also a repeat of what Intel attempted with its previous Arc GPUs.

Intel's "Arrow Lake" processor plans include both desktop and laptop chips, creating two new performance tiers atop its existing "Lunar Lake" laptop processors plus the Core Ultra 200S desktop processors it's launching today. Intel disclosed plans for three different product families: the Arrow Lake-S desktop processor as well as the Arrow Lake-HX and Arrow Lake-H processors for laptops. While Intel will begin taking orders for the Core Ultra 200S (Arrow Lake-S) family beginning on October 24, Intel will ship its notebook processors in the first quarter of 2025. A year ago, Intel introduced its 14th-gen desktop processors at an October launch event, then followed with lower-power desktop offerings at CES 2024. All three Arrow Lake families strongly emphasize energy efficiency.

If you're wondering why you need Intel's latest Core Ultra laptop processors, code-named Lunar Lake, the answer isn't a simple one. But if you want to buy a Lunar Lake laptop, however, you'll probably understand the differences between the various chips almost immediately. Intel unveiled what it's calling the Core Ultra Series 2 at a launch event in Berlin on Tuesday. In total, there are nine new Lunar Lake processors, which will begin shipping inside laptops from PC makers like Acer, Asus, HP, and Lenovo very soon: September 24. A few months ago, Intel unveiled the guts of the new Lunar Lake architecture, offering a sneak peek into how much performance it will offer and how much power it will consume.

In small satellites there is less room for heat control equipment, scientific instruments, and electronic components. Furthermore, the near proximity of the electronics makes power dissipation difficult, with the risk of not being able to control the temperature appropriately, reducing component lifetime and mission performance. To address this challenge, taking advantage of the advent of increasing intelligence on board satellites, a deep reinforcement learning based framework that uses Soft Actor-Critic algorithm is proposed for learning the thermal control policy onboard. The framework is evaluated both in a naive simulated environment and in a real space edge processing computer that will be shipped in the future IMAGIN-e mission and hosted in the ISS. The experiment results show that the proposed framework is able to learn to control the payload processing power to maintain the temperature under operational ranges, complementing traditional thermal control systems.

As we walk into the age of AI, there is an exponential rise in the demand for GPU. The not-so-old method of parallel computing is applied to process computations in GPUs. Moreover, with the availability of very high numbers of ALUs or processing units, GPUs have become very suitable for powerful computations in AI. Furthermore, with the recent advent of Deep Learning in the current decade, most of the Deep Learning frameworks, including vastly popular TensorFlow, Pytorch, Theano, etc., enable advanced optimization of computations with GPU. Currently, a vast number of GPUs are available, with many differences in their features, like no. of processing units, memory capacity, clock frequency, etc.

That was an easy answer just a few years ago, when Intel's product line was far and away the strongest. But with multiple generations of AMD's game-changing Ryzen chips finally giving Intel some real competition, you have more to think about. We're here to help you navigate this wider landscape, but without thousands of words and stacks of charts. We'll start with a quick primer on the strengths and weaknesses of each chip, then we'll discuss how to pick the right one for you. To keep this from getting too overwhelming, we'll stick only to the mainstream CPUs that typically go into three-pound, thin-and-light laptops, rather than get into the high-performance chips that go into thicker and heavier gaming laptops.

Google may be buying heavens only knows how many GPUs to run HPC and AI workloads on its eponymous public cloud, and it may have talked recently about how it is committed to the idea of pushing the industry to innovate at the SoC level and staying out of designing its own compute engines, but the company is still building its own Tensor Processing Units, or TPUs for short, to support its TensorFlow machine learning framework and the applications it drives within Google and as a service for Google Cloud customers. If you were expecting to get a big reveal of the TPUv4 architecture from the search engine giant and machine learning pioneer at its Google I/O 2021 conference this week, you were no doubt, like us, sorely disappointed. In his two-hour keynote address, which you can see here, Google chief executive officer Sundar Pichai, who is also CEO at Google's parent company, Alphabet, ever so briefly talked about the forthcoming TPUv4 custom ASIC designed by Google and presumably built by Taiwan Semiconductor Manufacturing Corp like every other leading-edge compute engine on Earth. As the name suggests, the TPUv4 chip is Google's fourth generation of machine learning Bfloat processing beasts, which it weaves together with host systems and networking to create what amounts to a custom supercomputer. "This is the fastest system that we have ever deployed at Google – a historic milestone for us," Pichai explained in his keynote.

What's the best CPU to get in your next laptop? That question became a lot harder to answer earlier this year when AMD unleashed its Ryzen 4000 mobile CPUs, finally bringing some real competition against Intel's Core product line. If that weren't enough, Intel has multiple generations of Core mobile CPU on the market, each with specific strengths and weaknesses. We're here to help you navigate this chaotic landscape, but without thousands of words and stacks of charts. We'll start with a quick primer on the strengths and weaknesses of each chip, then we'll discuss how to pick the right one for you.