Edge Embedded Devices Running by Weaver Byte (10 results)
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
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- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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More images- Softcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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Paperback. Condition: New.
- Softcover
- Print on Demand
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
- Print on Demand
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. What if your AI model has to run on a device with less RAM than a single smartphone photo? Edge AI on Embedded Devices answers that question with engineering discipline, not theory.Why this matters now: Billions of microcontrollers power our world-pacemakers, industrial sensors, smart infras…tructure. Cloud AI can't reach them. This book shows how to build machine learning systems that thrive under constraints where standard ML practices break down.What makes this different: Concrete trade-offs between accuracy, latency, memory, and power consumption on real hardwareModel optimization techniques that preserve performance when kilobytes matterDeployment pipelines designed for resource-limited targets, not GPU clustersSecurity and maintenance strategies for devices in the field for decadesHardware selection frameworks that match model complexity to silicon capabilitiesSystems-level thinking: Connects model architecture to power management, real-time OS behavior, and long-term reliability. No abstraction comes without cost analysis.For practitioners: Written for engineers building production systems, not running benchmarks. Embedded developers learn ML constraints. ML engineers learn embedded realities. Both learn to design AI that survives deployment.Build AI that runs where cloud computing ends. Start designing systems engineered for silicon, not slides. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
US$ 43.60
US$ 49.75 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. What if your AI model has to run on a device with less RAM than a single smartphone photo? Edge AI on Embedded Devices answers that question with engineering discipline, not theory.Why this matters now: Billions of microcontrollers power our world-pacemakers, industrial sensors, smart infras…tructure. Cloud AI can't reach them. This book shows how to build machine learning systems that thrive under constraints where standard ML practices break down.What makes this different: Concrete trade-offs between accuracy, latency, memory, and power consumption on real hardwareModel optimization techniques that preserve performance when kilobytes matterDeployment pipelines designed for resource-limited targets, not GPU clustersSecurity and maintenance strategies for devices in the field for decadesHardware selection frameworks that match model complexity to silicon capabilitiesSystems-level thinking: Connects model architecture to power management, real-time OS behavior, and long-term reliability. No abstraction comes without cost analysis.For practitioners: Written for engineers building production systems, not running benchmarks. Embedded developers learn ML constraints. ML engineers learn embedded realities. Both learn to design AI that survives deployment.Build AI that runs where cloud computing ends. Start designing systems engineered for silicon, not slides. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
