1,721,050 research outputs found
Wireless Memory Test: A Breakthrough Solution for Highly Reliable HBM
No full textThe invention of high bandwidth memory (HBM) has led the development of high performance computing and artificial intelligence algorithms. However, critical problems to limit the next-generation HBM are not only signal, power, and thermal integrity, but also testability and reliability. In this paper, we proposed a wireless memory test scheme as a breakthrough solution for highly reliable next-generation HBM. The proposed scheme can wirelessly transfer the input test signal using a 300 GHz band on-chip patch antenna. A QPSK receiver integrated on the HBM logic die achieves a data rate of 2 Gbps with BER 10-9. The proposed wireless test scheme can achieve testability and increase the reliability by elimination of ATE to DUT channel loss in the conventional test system. Furthermore, existing direct access (DA) pads for test can be converted to signal and power pads with the wireless transmission of test signal. The additional signal and power pads can basically enhance the signal and power integrity of HBM. Especially in the conventional HBM pad map, the placement of test pads limits the directivity of IO interface. However, the proposed wireless test scheme enables a new interface architecture and reinvention of the existing HBM
Video Probabilistic Diffusion Models in Projected Latent Space
No full textDespite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality and complex temporal dynamics along with large spatial variations. Recent works on diffusion models have shown their potential to solve this challenge, yet they suffer from severe computation and memory-inefficiency that limit the scalability. To handle this issue, we propose a novel generative model for videos, coined projected latent video diffusion model (PVDM), a probabilistic diffusion model which learns a video distribution in a low-dimensional latent space and thus can be efficiently trained with high-resolution videos under limited resources. Specifically, PVDM is composed of two components: (a) an autoencoder that projects a given video as 2D-shaped latent vectors that factorize the complex cubic structure of video pixels and (b) a diffusion model architecture specialized for our new factorized latent space and the training/sampling procedure to synthesize videos of arbitrary length with a single model. Experiments on popular video generation datasets demonstrate the superiority of PVDM compared with previous video synthesis methods; e.g., PVDM obtains the FVD score of 639.7 on the UCF-101 long video (128 frames) generation benchmark, which improves 1773.4 of the prior state-of-the-art
How to Manage TDMA Power Amplifier Switching Noise Coupling to Audio CODECs in Mobile Phones
No full textSignal and Power Integrity (SI/PI) Analysis of Heterogeneous Integration Using Embedded Multi-die Interconnect Bridge (EMIB) Technology for High Bandwidth Memory (HBM)
No full textLearning Large-scale Neural Fields via Context Pruned Meta-Learning
No full textWe introduce an efficient optimization-based meta-learning technique for large-scale neural field training by realizing significant memory savings through automated online context point selection. This is achieved by focusing each learning step on the subset of data with the highest expected immediate improvement in model quality, resulting in the almost instantaneous modeling of global structure and subsequent refinement of high-frequency details. We further improve the quality of our meta-learned initialization by introducing a bootstrap correction resulting in the minimization of any error introduced by reduced context sets while simultaneously mitigating the well-known myopia of optimization-based meta-learning. Finally, we show how gradient re-scaling at meta-test time allows the learning of extremely high-quality neural fields in significantly shortened optimization procedures. Our framework is model-agnostic, intuitive, straightforward to implement, and shows significant reconstruction improvements for a wide range of signals. We provide an extensive empirical evaluation on nine datasets across multiple multiple modalities, demonstrating state-of-the-art results while providing additional insight through careful analysis of the algorithmic components constituting our method. Code is available at https://github.com/jihoontack/GradNCP
Power Distribution Network (PDN) Design and Analysis of A Single and. Double-Sided High Bandwidth Memory (HBM) Interposer for 2.5D Terabtye/s Bandwidth System
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Design and Analysis of Power Distribution Network (PDN) for High Bandwidth Memory (HBM) Interposer in 2.5D Terabyte/s Bandwidth Graphics Module
No full textElectromagnetic Bandgap Design for Power Distribution Network Noise Isolation in the Glass Interposer
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