Architecture and Circuit Design Optimization for Compute-In-Memory

The objective of the proposed research is to optimize computing-in-memory (CIM) design for accelerating Deep Neural Network (DNN) algorithms. As compute peripheries such as analog-to-digital converter (ADC) introduce significant overhead in CIM inference design, the research first focuses on the circuit optimization for inference acceleration and proposes a resistive random access memory (RRAM) based ADC-free in-memory compute scheme. We comprehensively explore the trade-offs involving different types of ADCs and investigate a new ADC design especially suited for the CIM, which performs the analog shift-add for multiple weight significance bits, improving the throughput and energy efficiency under similar area constraints. Furthermore, we prototype an ADC-free CIM inference chip design with a fully-analog data processing manner between sub-arrays, which can significantly improve the hardware performance over the conventional CIM designs and achieve near-software classification accuracy on ImageNet and CIFAR-10/-100 dataset. Secondly, the research focuses on hardware support for CIM on-chip training. To maximize hardware reuse of CIM weight stationary dataflow, we propose the CIM training architectures with the transpose weight mapping strategy. The cell design and periphery circuitry are modified to efficiently support bi-directional compute. A novel solution of signed number multiplication is also proposed to handle the negative input in backpropagation. Finally, we propose an SRAM-based CIM training architecture and comprehensively explore the system-level hardware performance for DNN on-chip training based on silicon measurement results.Ph.D

Micro-patterned TiO2 films for photocatalysis

Titanium dioxide film as a stable material plays an important role in photocatalytic degradation of pollutants. One of the ways to improve photocatalytic efficiency is to increase the active sites on the semiconductor surface. Photolithography is a manufacturing technique for controlling precisely micrographics on surface. Here grating-structured, square-structured and hexagon-structured TiO2 films were prepared by photolithography and the effects of various surface structures on photocatalysis were studied. It was demonstrated that the photocatalytic activity of TiO2 film was not always improved as the surface area increased. The micro-patterned surface would also impede the mass transfer in the process of photocatalysis. (C) 2019 Elsevier B.V. All rights reserved

NeuroSim Simulator for Compute-in-Memory Hardware Accelerator: Validation and Benchmark

Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware accelerators. A simulator with options of various mainstream and emerging memory technologies, architectures, and networks can be a great convenience for fast early-stage design space exploration of CIM hardware accelerators. DNN+NeuroSim is an integrated benchmark framework supporting flexible and hierarchical CIM array design options from a device level, to a circuit level and up to an algorithm level. In this study, we validate and calibrate the prediction of NeuroSim against a 40-nm RRAM-based CIM macro post-layout simulations. First, the parameters of a memory device and CMOS transistor are extracted from the foundry’s process design kit (PDK) and employed in the NeuroSim settings; the peripheral modules and operating dataflow are also configured to be the same as the actual chip implementation. Next, the area, critical path, and energy consumption values from the SPICE simulations at the module level are compared with those from NeuroSim. Some adjustment factors are introduced to account for transistor sizing and wiring area in the layout, gate switching activity, post-layout performance drop, etc. We show that the prediction from NeuroSim is precise with chip-level error under 1% after the calibration. Finally, the system-level performance benchmark is conducted with various device technologies and compared with the results before the validation. The general conclusions stay the same after the validation, but the performance degrades slightly due to the post-layout calibration

Density effects on nanoparticle transport in the hyporheic zone

A carbon solution composed of nanoparticles was used in experiments designed to explore nanoparticle transport characteristics within the hyporheic zone of a riverbed. Experiments and numerical simulations demonstrated that nanoparticle transport in the hyporheic zone is affected by hydraulic head gradients due to river flow-bedform interactions as well as density gradients associated with the nano-carbon solution. Differences with similar flow/transport situations were examined, and it was found that particulate-enhanced density can change hyporheic transport appreciably. In addition to density, particle settling enhances downward movement of the nano-carbon plume in the riverbed. While nanoparticle transport in the upper hyporheic zone is largely controlled by advection due to flow driven by head gradients at the bed surface, density gradients and particle settling influence the transport process significantly in the lower hyporheic zone. During the transport process, nanoparticles become deposited due to attachment to sand particles and filtration by small pores in the bed. Compared with transport where density variations are minimal, the particulate-induced density gradient induces downward transport of nanoparticles and entrained liquids, leading to deposition/accumulation at the base of the bed

Determination of key enzymes for threonine synthesis through in vitro metabolic pathway analysis

Figure S1. The pathway flux (J) in the in vitro system when one enzyme concentration was increased. (A) The pathway flux when purified ThrA was added to the crude enzyme extract. (B) The pathway flux when purified Asd was added to the crude enzyme extract. (C) The pathway flux when purified ThrB was added to the crude enzyme extract. (D) The pathway flux when purified ThrC was added to the crude enzyme extract

MicroRNA let-7c Is Downregulated in Prostate Cancer and Suppresses Prostate Cancer Growth

Prostate cancer (PCa) is characterized by deregulated expression of several tumor suppressor or oncogenic miRNAs. The objective of this study was the identification and characterization of miR-let-7c as a potential tumor suppressor in PCa.Levels of expression of miR-let-7c were examined in human PCa cell lines and tissues using qRT-PCR and in situ hybridization. Let-7c was overexpressed or suppressed to assess the effects on the growth of human PCa cell lines. Lentiviral-mediated re-expression of let-7c was utilized to assess the effects on human PCa xenografts.We identified miR-let-7c as a potential tumor suppressor in PCa. Expression of let-7c is downregulated in castration-resistant prostate cancer (CRPC) cells. Overexpression of let-7c decreased while downregulation of let-7c increased cell proliferation, clonogenicity and anchorage-independent growth of PCa cells in vitro. Suppression of let-7c expression enhanced the ability of androgen-sensitive PCa cells to grow in androgen-deprived conditions in vitro. Reconstitution of Let-7c by lentiviral-mediated intratumoral delivery significantly reduced tumor burden in xenografts of human PCa cells. Furthermore, let-7c expression is downregulated in clinical PCa specimens compared to their matched benign tissues, while the expression of Lin28, a master regulator of let-7 miRNA processing, is upregulated in clinical PCa specimens.These results demonstrate that microRNA let-7c is downregulated in PCa and functions as a tumor suppressor, and is a potential therapeutic target for PCa

Bienzyme system immobilized in biomimetic silica for application in antifouling coatings

Antifouling coatings are used extensively on vessels and underwater structures. Conventional antifouling coatings contain toxic biocides and heavy metals, which may induce unwanted adverse effects such as toxicity to non target organisms, imposex in gastropods and increased multiresistance among bacteria. Therefore, enzyme-based coatings could be a new alternative solution. A H2O2-producing bienzyme system was developed in this study. H2O2 can be produced from starch by the cooperation of a-amylase and glucose oxidase, which promotes the hydrolysis of polymeric chain and oxidizes the glucose to produce H2O2, respectively. The encapsulated bienzyme (A-G@BS) exhibits enhanced stabilities of thermal, pH, recycling and tolerance of xylene. The A-G@BS-containing coating releases H2O2 at rates exceeding a target of 36 nmol.cm(-2).d(-1) for 90 days in a laboratory assay. The results demonstrate that the method is a promising coating technology for entrapping active enzymes, presenting an interesting avenue for enzyme-based antifouling solutions. (C) 2015 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved