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We extensively tested the analog architecture with simulations at transistor level by using the netlist extracted from the physical design.
These results highlight the lower robustness of these two algorithms when implemented in our analog architecture.
The developed algorithms based on low-level computations are designed to be implemented on a versatile analog architecture allowing a wide range of operators and compact processing steps.
In this subsection, we discuss the possibility of user separation in our analog architecture when there exist the multiple paths in mmWave channel.
Thus we can consider that the recursive average-based methods are more robust than the ones based on modulations (, SBA), when implemented in our analog architecture.
Compared to classical sensors performing motion detection downstream the image acquisition, the offered processing capabilities are somehow limited, but the chosen analog architecture, on which they are implemented, offers a better compromise between power consumption and algorithm performance.
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This paper presents a capacitor based Digital to Analog Converter architecture, which gives comparable performance with the conventional architecture with approximately half the total capacitance.
The fully parallel and analog network architecture of the sensor guarantees efficient real-time visual processing.
We present the analog circuit architecture of a feed-forward network with on-chip weight perturbation learning in CMOS technology.
Novelty of the approach lies in the circuit implementation of the feed-forward neural primitives and on the overall analog circuit architecture.
We have experimentally demonstrated small-signal system bandwidth of 10 kHz using optical quad-cell feedback and a two-channel analog control architecture.
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