Fossil Sport Smartwatch with Snapdragon Wear 3100 SoC, all-day battery life

A few months ago, Qualcomm announced the new Snapdragon Wear 3100 SoC for next-generation smartwatches and promises better battery life. To capitalize on this claim, Fossil has now announced the Snapdragon Wear 3100 Sport Smartwatch. This is the first smart watch to hit the market with the new SoC and claims to provide battery life all day. The Fossil Sport Smartwatch has a round dial, a replaceable silicone strap and is available in different color options for users.

The Fossil Sport Smartwatch runs on the latest Wear OS platform and should also be safe from flooding. The price of the silicone and leather straps is $ 255 (approximately Rs 18,500) and the stainless steel bracelet is $ 275 (Rs approximately 20,000). There are six different color options – Gray, Pink, Red, Blue, Green and Black. The Smarwatch is available in two dial sizes – 41mm and 43mm – and features 28 interchangeable silicone bands (available in 18mm and 22mm sizes). The Fossil Sport Smartwatch is already online and available in partner stores in the US.

The new Fossil Sport Smartwatch features a floating design and supports Google Pay, GPS to track all types of activity, and heart rate monitoring. It supports customizable dials and multiple dials to suit users' preferences. Connectivity options include Bluetooth version 4.1 and Wi-Fi 802.11 b / g / n. The wearable is compatible with Android and iOS smartphones, specifically Android OS 4.4 Kit Kat and above (except Go Edition) or iOS 9.3 and above.

As mentioned previously, fossil indicates that the wearable can last up to a day. In energy-saving mode, the battery can be extended up to two days. With the new Wear OS platform, Fossil Sport Smartwatch also has a Google Assistant for voice support.

As a reminder, Qualcomm introduced the Snapdragon Wer 3100 SoC in September this year. consists of quad-core A7 processors, an integrated DSP and an extremely economical co-processor QCC1110. This coprocessor is tiny, optimized for ultra-low-power operation, and integrates a deep learning engine for custom workloads, such as:

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