Large-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical computing. Traditional experimen
Space-efficient optical computing with an integrated chip diffractive neural networkElectronic devicesIntegrated opticsSilicon photonicsLarge-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical...
That is, the model estimates an optical flow field and uses the current field to warp the spatial information of the original input frame or feature map, and so on. We encode the time-step t as a separate channel [28, 39], and feed it into t...
AgeDETR was trained by using the AdamW optimizer on an NVIDIA RTX 2080 Ti GPU with a batch size of 4. The training regimen spanned 300 epochs, starting with an initial learning rate of 0.0001 and a weight decay rate of 0.0001. We trained the model with 640×640640×640 pixel images to...
Head-mounted optical see-through displays have recently become an affordable technology and are a promising platform for volumetric image visualisation. Images displayed on a head-mounted display must be presented at a high frame rate and with low latency to compensate for head motion. High latency...
FIG. 4 illustrates an underfill region414that may be representative of the implementation of the underfilling process of FIG. 2 according to some embodiments. With respect to any given underfilling process, the term “underfill region” may be used to collectively denote any space on the surface...
1. An apparatus, comprising: a processor, wherein the processor includes: an execution pipeline; and one or more sets of checkpoint storage locations configured to store state information associated with up to a maximum of N checkpoints that the processor is configured to take in response to de...
The proposed heterogeneous implementation is able to achieve power savings up to 10% and 50% in the Spanke and multi-stage Bene architectures, respectively, with respect to SOA-based space-switch implementations. Moreover, an improvement of the physical layer performance is achievable in the Spank...
Near real-time detection is obtained using General Purpose computing on Graphics Processing Units (GPGPU). The high degree of processing parallelism provided by GPGPU allows to split data analysis over thousands of threads in order to process big datasets with a limited computational time. The ...
[24], Eq. 5Footnote6) for each pixel with a temporally recursive motion-compensated 3-tap filter. However, this approach is far from practical, since it either requires memory for storing at least two 255-bin histograms for each pixel in an image, or recomputing the histograms on the fly...