First off, NVIDIA's next-gen Hopper GPU isn't a gaming architecture -- so we're not going to see the GeForce RTX 4080 Ti or something based on the Hopper GPU... but we have the most exciting GPU ...
When it comes to multi-chip module (MCM) manufacturing, fan-out wafer-level and fan-out panel-level packaging have received a lot of coverage recently. Every week, it seems like there is an ...
Ithaca, NY—Traquair Data Systems, Inc. announces the availability of the ultra-compact UC1394a-1 multi-chip module in a general-purpose embedded DSP configuration, with supporting EVM-Kits for DSP and ...
Multi-chip module (MCM) graphics cards just might be the future, bringing upcoming generations of GPUs to a whole new level. Combining multiple dies instead of relying on a single chip could provide ...
AMD is reportedly gearing up for its new Instinct MI200 compute accelerator -- note the lack of the Radeon brand here -- with its new CDNA-based accelerator reportedly rocking an MCM (multi-chip ...
Energy/bit optimization approach for multi-chip systems with possibility of co-optimization with the routing resources defined by the signalling pitch. December 7th, 2022 - By: Fraunhofer IIS/EAS More ...
New-generation Airfast RF Multi-Chip Modules (MCMs) extend frequency coverage to 4.0 GHz, leveraging the performance of NXP’s latest LDMOS technology and integration design techniques Higher output ...
Qorvo, Inc. has launched three new highly integrated RF multi-chip modules (MCMs) designed for advanced radar applications. Leveraging Qorvo’s advanced packaging and process technology, the QPF5001, ...
Historically, GPUs have been designed as monolithic dies with all of their functionality under one 'roof.' This hasn't always been the case -- the earliest GPUs sometimes used separate chips for ...
From Toshiba America Electronic Components, the TB7004FL multi-chip module (MCM) contains the gate drivers and MOSFETs required to build a 35-A, 30-V synchronous buck converter. From Toshiba America ...
Why it matters: Currently available deep learning resources are falling behind the curve due to increasing complexity, diverging resource requirements, and limitations imposed by existing hardware ...
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