FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture

Neural Network (NN) accelerators with emerging ReRAM (resistive random access memory) technologies have been investigated as one of the promising solutions to address the \textit{memory wall} challenge, due to the unique capability of \textit{processing-in-memory} within ReRAM-crossbar-based processing elements (PEs). However, the high efficiency and high density advantages of ReRAM have not been fully utilized due to the huge communication demands among PEs and the overhead of peripheral circuits. In this paper, we propose a full system stack solution, composed of a reconfigurable architecture design, Field Programmable Synapse Array (FPSA) and its software system including neural synthesizer, temporal-to-spatial mapper, and placement & routing. We highly leverage the software system to make the hardware design compact and efficient. To satisfy the high-performance communication demand, we optimize it with a reconfigurable routing architecture and the placement & routing tool. To improve the computational density, we greatly simplify the PE circuit with the spiking schema and then adopt neural synthesizer to enable the high density computation-resources to support different kinds of NN operations. In addition, we provide spiking memory blocks (SMBs) and configurable logic blocks (CLBs) in hardware and leverage the temporal-to-spatial mapper to utilize them to balance the storage and computation requirements of NN. Owing to the end-to-end software system, we can efficiently deploy existing deep neural networks to FPSA. Evaluations show that, compared to one of state-of-the-art ReRAM-based NN accelerators, PRIME, the computational density of FPSA improves by 31x; for representative NNs, its inference performance can achieve up to 1000x speedup.Comment: Accepted by ASPLOS 201

Lumbar spine localisation method based on feature fusion

To eliminate unnecessary background information, such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning, a two-stage approach for localising lumbar segments is proposed. First, based on the multi-scale feature fusion technology, a non-linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine, effectively eliminating useless background information, such as soft tissues. In the second stage, we directly realised the precise positioning of each segment in the lumbar spine space region based on the non-linear regression method, thus effectively eliminating the interference caused by the adjacent spine. The 3D Intersection over Union (3D_IOU) is used as the main evaluation indicator for the positioning accuracy. On an open dataset, 3D_IOU values of 0.8339 ± 0.0990 and 0.8559 ± 0.0332 in the first and second stages, respectively is achieved. In addition, the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s respectively. Therefore, the proposed method performs very well in terms of both precision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning

Stem Cell Factor SALL4 Represses the Transcriptions of PTEN and SALL1 through an Epigenetic Repressor Complex

Background The embryonic stem cell (ESC) factor, SALL4, plays an essential role in both development and leukemogenesis. It is a unique gene that is involved in self-renewal in ESC and leukemic stem cell (LSC).Methodology/Principal Findings To understand the mechanism(s) of SALL4 function(s), we sought to identify SALL4-associated proteins by tandem mass spectrometry. Components of a transcription repressor Mi-2/Nucleosome Remodeling and Deacetylase (NuRD) complex were found in the SALL4-immunocomplexes with histone deacetylase (HDAC) activity in ESCs with endogenous SALL4 expression and 293T cells overexpressing SALL4. The SALL4-mediated transcriptional regulation was tested on two potential target genes: PTEN and SALL1. Both genes were confirmed as SALL4 downstream targets by chromatin-immunoprecipitation, and their expression levels, when tested by quantitative reverse transcription polymerase chain reaction (qRT-PCR), were decreased in 293T cells overexpressing SALL4. Moreover, SALL4 binding sites at the promoter regions of PTEN and SALL1 were co-occupied by NuRD components, suggesting that SALL4 represses the transcriptions of PTEN and SALL1 through its interactions with the Mi-2/NuRD complex. The in vivo repressive effect(s) of SALL4 were evaluated in SALL4 transgenic mice, where decreased expressions of PTEN and SALL1 were associated with myeloid leukemia and cystic kidneys, respectively.Conclusions/Significance In summary, we are the first to demonstrate that stem cell protein SALL4 represses its target genes, PTEN and SALL1, through the epigenetic repressor Mi-2/NuRD complex. Our novel finding provides insight into the mechanism(s) of SALL4 functions in kidney development and leukemogenesis

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Total Least-Squares Iterative Closest Point Algorithm Based on Lie Algebra

In geodetic surveying, input data from two coordinates are needed to compute rigid transformations. A common solution is a least-squares algorithm based on a Gauss–Markov model, called iterative closest point (ICP). However, the error in the ICP algorithm only exists in target coordinates, and the algorithm does not consider the source model’s error. A total least-squares (TLS) algorithm based on an errors-in-variables (EIV) model is proposed to solve this problem. Previous total least-squares ICP algorithms used a Euler angle parameterization method, which is easily affected by a gimbal lock problem. Lie algebra is more suitable than the Euler angle for interpolation during an iterative optimization process. In this paper, Lie algebra is used to parameterize the rotation matrix, and we re-derive the TLS algorithm based on a GHM (Gauss–Helmert model) using Lie algebra. We present two TLS-ICP models based on Lie algebra. Our method is more robust than previous TLS algorithms, and it suits all kinds of transformation matrices

Changes in Pore Structure of Coal Caused by CS2 Treatment and Its Methane Adsorption Response

The pore structure and gas adsorption are two key issues that affect the coal bed methane recovery process significantly. To change pore structure and gas adsorption, 5 coals with different ranks were treated by CS2 for 3 h using a Soxhlet extractor under ultrasonic oscillation conditions; the evolutions of pore structure and methane adsorption were examined using a high-pressure mercury intrusion porosimeter (MIP) with an AutoPore IV 9310 series mercury instrument. The results show that the cumulative pore volume and specific surface area (SSA) were increased after CS2 treatment, and the incremental micropore volume and SSA were increased and decreased before and after Ro,max=1.3%, respectively; the incremental big pore (greater than 10 nm in diameter) volumes were increased and SSA was decreased for all coals, and pore connectivity was improved. Methane adsorption capacity on coal before and after Ro,max=1.3% also was increased and decreased, respectively. There is a positive correlation between the changes in the micropore SSA and the Langmuir volume. It confirms that the changes in pore structure and methane adsorption capacity due to CS2 treatment are controlled by the rank, and the change in methane adsorption is impacted by the change of micropore SSA and suggests that the changes in pore structure are better for gas migration; the alteration in methane adsorption capacity is worse and better for methane recovery before and after Ro,max=1.3%. A conceptual mechanism of pore structure is proposed to explain methane adsorption capacity on CS2 treated coal around the Ro,max=1.3%