ABSTRACT
Learning of hierarchical features with spiking neurons has mostly been investigated in the database framework of standard deep learning systems. However, the properties of neuromorphic systems could be particularly interesting for learning from continuous sensor data in real-world settings. In this work, we introduce a deep spiking convolutional neural network of integrate-and-fire (IF) neurons which performs unsupervised online deep learning with spike-timing dependent plasticity (STDP) from a stream of asynchronous and continuous event-based data. In contrast to previous approaches to unsupervised deep learning with spikes, where layers were trained successively, we introduce a mechanism to train all layers of the network simultaneously. This allows approximate online inference already during the learning process and makes our architecture suitable for online learning and inference. We show that it is possible to train the network without providing implicit information about the database, such as the number of classes and the duration of stimuli presentation. By designing an STDP learning rule which depends only on relative spike timings, we make our network fully event-driven and able to operate without defining an absolute timescale of its dynamics. Our architecture requires only a small number of generic mechanisms and therefore enforces few constraints on a possible neuromorphic hardware implementation. These characteristics make our network one of the few neuromorphic architecture which could directly learn features and perform inference from an event-based vision sensor.
ABSTRACT
Liquid crystal (LC) tunable lenses have been extensively studied and used in various applications, however, most of them have been evaluated regardless of their optical imaging quality, in particular, concerning their intrinsic diffuse scattering. In this paper, we investigate the impact of such impairments when LC lenses are used as tunable elements in a depth-from-focus algorithm (DfF). We attempt to analyze these effects in order to design LC lenses that mitigate their impact on the imaging quality. For this purpose, we designed various lenses to evaluate several parameters such as optical, electrical, manufacturing, etc., according to their implementations in a near-pixel DfF architecture.
ABSTRACT
This paper presents the first low noise complementary metal oxide semiconductor (CMOS) deletedCMOS terahertz (THz) imager based on source modulation and in-pixel high-Q filtering. The 31 × 31 focal plane array has been fully integrated in a 0 . 13 µ m standard CMOS process. The sensitivity has been improved significantly by modulating the active THz source that lights the scene and performing on-chip high-Q filtering. Each pixel encompass a broadband bow tie antenna coupled to an N-type metal-oxide-semiconductor (NMOS) detector that shifts the THz radiation, a low noise adjustable gain amplifier and a high-Q filter centered at the modulation frequency. The filter is based on a passive switched-capacitor (SC) N-path filter combined with a continuous-time broad-band Gm-C filter. A simplified analysis that helps in designing and tuning the passive SC N-path filter is provided. The characterization of the readout chain shows that a Q factor of 100 has been achieved for the filter with a good matching between the analytical calculation and the measurement results. An input-referred noise of 0 . 2 µ V RMS has been measured. Characterization of the chip with different THz wavelengths confirms the broadband feature of the antenna and shows that this THz imager reaches a total noise equivalent power of 0 . 6 nW at 270 GHz and 0 . 8 nW at 600 GHz.
