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

A novel approach to pulmonary bronchial tree model construction and performance index study

The demand for respiratory disease and dynamic breathing studies has continuously driven researchers to update the pulmonary bronchial tree’s morphology model. This study aims to construct a bronchial tree morphology model efficiently and effectively with practical algorithms. We built a performance index system using failure branch rate, volume ratio, and coefficient of variation of terminal volumes to evaluate the model performance. We optimized the parameter settings and found the best options to build the morphology model, and we constructed a 14th-generation bronchial tree model with a decent performance index. The dimensions of our model closely matched published data from anatomic in vitro measurements. The proposed model is adjustable and computable and will be used in future dynamic breathing simulations and respiratory disease studies

Memory-Centric Architectures: Bridging the Gap Between Compute and Memory

While the compute part keeping scaling for decades, it becomes more and more difficult for the memory part to catch up. This mismatch raises two grand challenges. One is referred to as the "Memory Wall", in which case the memory latency and bandwidth turn to be the bottleneck, slowing down the system no matter how computing resource improves. The other one is referred to as the ``Power Wall'', which demands high power efficiency due to a limited power budget, whereas the energy spent on the memory accesses dominates the total energy consumption.To address those challenges, this dissertation focuses on designing memory-centric architectures to bridge the gap between compute and memory. Two types of memory-centric architecture have been investigated. The first one is the compute-capable memory architecture, which moves computing resources to the memory side. The in-memory computing scheme explores larger bandwidth and reduces data movement overhead. The second one is the memory-rich accelerator architecture, which is designed with tightly coupled high performance computing resource and large-capacity on-die memory. The in-situ computing design provides benefits as a non Von Neumann architecture. This dissertation has proposed five architectures, which cover both compute-capable memory and memory-rich accelerator architectures, both the offshore DRAM and emerging non-volatile memory technologies, and a large range of the important applications, such as deep learning, database, graph processing

Memory-Centric Architectures: Bridging the Gap Between Compute and Memory

While the compute part keeping scaling for decades, it becomes more and more difficult for the memory part to catch up. This mismatch raises two grand challenges. One is referred to as the "Memory Wall", in which case the memory latency and bandwidth turn to be the bottleneck, slowing down the system no matter how computing resource improves. The other one is referred to as the ``Power Wall'', which demands high power efficiency due to a limited power budget, whereas the energy spent on the memory accesses dominates the total energy consumption.To address those challenges, this dissertation focuses on designing memory-centric architectures to bridge the gap between compute and memory. Two types of memory-centric architecture have been investigated. The first one is the compute-capable memory architecture, which moves computing resources to the memory side. The in-memory computing scheme explores larger bandwidth and reduces data movement overhead. The second one is the memory-rich accelerator architecture, which is designed with tightly coupled high performance computing resource and large-capacity on-die memory. The in-situ computing design provides benefits as a non Von Neumann architecture. This dissertation has proposed five architectures, which cover both compute-capable memory and memory-rich accelerator architectures, both the offshore DRAM and emerging non-volatile memory technologies, and a large range of the important applications, such as deep learning, database, graph processing

Genetic Components of Self-Incompatibility in Brassica Vegetables

Brassica vegetables are very important to human beings. Self-incompatibility (SI) is a common phenomenon in Brassica. Breeding by SI lines is an important way to utilize heterosis of Brassica vegetables. It is believed that the SI inheritance in Brassica species is controlled by three linkage genes on the S-locus, including SRK (S-locus receptor kinase), SCR (S-locus cystine-rich protein)/SP11 (S-locus protein 11), and SLG (S-locus glycoprotein). SRK is the female determinant and SCR/SP11 is the pollen S gene. The expression of SLG is necessary for SRK, and it enhances the SRK-mediated SI reaction. In addition to these three S-locus genes, some other functional molecules also have significant regulatory effects on SI, such as ARC1 (arm repeat containing 1), MLPK (M-locus protein kinase), Exo70A1 (exocyst compounds), THLl/THL2 (thioredoxin H-like), MOD (aquaporin), SLR (S-locus-related glycoprotein), BPCI (pollen calcium-binding protein I), etc. SI is also associated with the dominant/recessive relationship between S alleles. Here, the genetic elements and molecular mechanisms of SI, mainly in Brassica vegetables, are reviewed

Overexpression of E3 Ubiquitin Ligase Gene AdBiL Contributes to Resistance against Chilling Stress and Leaf Mold Disease in Tomato

Ubiquitination is a common regulatory mechanism, playing a critical role in diverse cellular and developmental processes in eukaryotes. However, a few reports on the functional correlation between E3 ubiquitin ligases and reactive oxygen species (ROS) or reactive nitrogen species (RNS) metabolism in response to stress are currently available in plants. In the present study, the E3 ubiquitin ligase gene AdBiL (Adi3 Binding E3 Ligase) was introduced into tomato line Ailsa Craig via Agrobacterium-mediated method. Transgenic lines were confirmed for integration into the tomato genome using PCR. Transcription of AdBiL in various transgenic lines was determined using real-time PCR. Evaluation of stress tolerance showed that T1 generation of transgenic tomato lines showed only mild symptoms of chilling injury as evident by higher biomass accumulation and chlorophyll content than those of non-transformed plants. Compared with wild-type plants, the contents of AsA, AsA/DHA, GSH and the activity of GaILDH, γ-GCS and GSNOR were increased, while H2O2, O2.−, MDA, NO, SNOs, and GSNO accumulations were significantly decreased in AdBiL overexpressing plants in response to chilling stress. Furthermore, transgenic tomato plants overexpressing AdBiL showed higher activities of enzymes such as G6PDH, 6PGDH, NADP-ICDH, and NADP-ME involved in pentose phosphate pathway (PPP). The transgenic tomato plants also exhibited an enhanced tolerance against the necrotrophic fungus Cladosporium fulvum. Tyrosine nitration protein was activated in the plants infected with leaf mold disease, while the inhibition could be recovered in AdBiL gene overexpressing lines. Taken together, our results revealed a possible physiological role of AdBiL in the activation of the key enzymes of AsA–GSH cycle, PPP and down-regulation of GSNO reductase, thereby reducing oxidative and nitrosative stress in plants. This study demonstrates an optimized transgenic strategy using AdBiL gene for crop improvement against biotic and abiotic stress factors