Minimum metal 1 feature size is around 660 nm with a 1225 nm pitch, metal 3 has larger 940 nm features with around 1400 nm pitch (however, overglass likely makes the wires on M3 appear fatter than the actual metal features are). M3-M2 vias do not have any visible sagging in the metal trace, but can be easily identified visually by a roughly 2000 nm circular capture pad on the conductor. Standard cell rows are about 9.9 μm tall, consistent with a technology node around 250 nm.
The first release of bitnet.cpp is to support inference on CPUs. bitnet.cpp achieves speedups of 1.37x to 5.07x on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by 55.4% to 70.0%, further boosting overall efficiency. On x86 CPUs, speedups range from 2.37x to 6.17x with energy reductions between 71.9% to 82.2%. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. Please refer to the technical report for more details.，这一点在wps中也有详细论述
，更多细节参见手游
FT Edit: Access on iOS and web。whatsapp是该领域的重要参考
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