SIXPPAC: mixed-signal streaming machine-learning engine for wireless baseband
A preprint presents SIXPPAC, a mixed-signal machine-learning engine for real-time integrated sensing and communication in wireless baseband. Its design combines two successive-approximation ADCs with a six-layer digital binary-weight neural network.
A preprint presents SIXPPAC, a mixed-signal machine-learning engine for real-time integrated sensing and communication in wireless baseband. Its design combines two successive-approximation ADCs with a six-layer digital binary-weight neural network.
The authors report a 0.765-square-millimetre 12-nanometre FinFET implementation operating at 105 million samples per second and 4.18 nanojoules per inference. These are paper-reported prototype measurements, not a commercial product specification.
At the BUILD 2026 conference, the communications minister cited power, connectivity, cloud access and advanced chips as constraints on local AI growth.
Source: Voice of Nigeria, reporting the Ministry of Communications, Innovation and Digital Economy
Researchers from Ottawa, Columbia and Hanyang reviewed post-CMOS materials and device approaches for artificial neurons; the work is a literature review, not a new chip prototype.
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