Simultaneous Power-Gating, Scheduling, and Binding in High-Level Synthesis with Lookahead Based Formation of Power Islands

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A Max Weighted Matching based Post-HLS Power Gating with Comprehensive Power Modeling

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On the Correlation between Resource Minimization and Interconnect Complexities in High-Level Synthesis

As the technology node of VLSI designs advances to sub10 nm, two interconnect-centric metrics of a circuit, the interconnect complexity (either number of interconnects or wirelength/WL) and congestion, become critically important across all design stages alongside conventional resource or function-unit (FU)-centric metrics like area/number-of-FUs and leakage power. High Level synthesis (HLS), one of the earliest and most impactful design stages, rarely monitors interconnect metrics, which makes their recovery at later stages very difficult. HLS algorithms and tools typically perform FU-centric minimization via operation scheduling, module selection (SMS) and binding. As a consequence, it mostly overlooks interconnect-based metrics. In this paper, we explore whether this can adversely affect interconnect metrics, and in general explore the correlation between FU-centric optimization in SMS, and the resulting interconnect metrics co-optimized (along with FU metrics) in the later binding stage(s). For this purpose we develop a probabilistic analysis for post-scheduling binding to estimate interconnect metrics, and verify its accuracy by comparison to empirical results across different scheduling techniques that generate different degrees of FU optimization. Based on both empirical and analytical results we predict how interconnects metrics will pan out with different degrees of FU optimization. Finally, based on our analysis, we also provide suggestions to improve interconnect metrics for whatever FU optimization degree an available SMS technique can achieve

A Power-Driven Stochastic-Deterministic Hierarchical High-Level Synthesis Framework for Module Selection, Scheduling and Binding

We present a power-driven hierarchical framework for module/functional-unit selection, scheduling, and binding in high level synthesis. A significant aspect of algorithm design for large and complex problems is arriving at tradeoffs between quality of solution and timing complexity. Towards this end, we integrate an improved version of the very runtime-efficient list scheduling algorithm called modified list scheduling (MLS) with a power-driven simulated annealing (SA) algorithm for module selection. Our hierarchical framework efficiently explores the problem solution space by an extensive exploration of the power-driven module-selection solution space via SA, and for each module selection solution, uses MLS to obtain a scheduling and (integrated) binding (S&B) solution in which the binding is either a regular one (minimizing number of FUs and thus FU leakage power) or power-driven with mux/demux power considerations. This framework avoids the very runtime intensive exploration of both module selection and S&B within a conventional SA algorithm, but retains the basic prowess of SA by exploring only the important aspect of power-driven module-selection in a stochastic manner. The proposed hierarchical framework provides an average of 9.5% FU leakage power improvement over state of the art (approximate) algorithms that optimize only FU leakage power, and has a smaller runtime by factors of 2.5–3x. Further, compared to a sophisticated flat simulated annealing framework and an optimal 0/1-ILP formulation for total (dynamic and leakage) FU and architecture power optimization under latency constraints, PSA-MLS provides an improvement of 5.3–5.8% with a runtime advantage of 2x, and has an average optimality gap of only 4.7–4.8% with a significant runtime advantage of a factor of more than 1900, respectively

Targeting GAS2 suppresses the <i>in vitro</i> and <i>in vivo</i> growth of MEG-01 cells.

<p>(A) The expression of <i>GAS2</i> in shNC (control), shGAS2#1 and shGAS2#2 transduced MEG-01 cells was measured with Q-RT-PCR. (B) The expression of GAS2 protein in shNC, shGAS2#1 and shGAS2#2 transduced cells were analyzed with FACS. (C) The calpain activities of various virally transduced cells were measured. (D) & (E) The proliferation and CFC production of various virally transduced cells were assessed. (F) Tumour growth of the Venus and GAS2DN expressed cells in nude mice were observed, and the fluorescence was monitored with IVIS II imaging system as well. (G) Representative pictures of the H & E staining of the tumours were presented. (H) The average sizes of the tumours formed by the Venus and GAS2DN expressed cells were compared. The tumour size was estimated with the equation: volume = 1/2 × length × width². Original magnification: ×200. *mean <i>p</i><0.05 and **mean <i>p</i><0.01, which were estimated with student <i>t</i>-test in a two-tailed fashion.</p

The knockdown of <i>HNRPDL</i> inhibits the growth of K562 cells.

<p>(A) The expression of <i>HNRPDL</i> was assessed with Q-RT-PCR in the virally transduced cells. (B) The expression of HNRPDL was analyzed with western blot. (C) & (D) The effect of HNRPDL silence on the proliferation and CFC production was analyzed. *mean <i>p</i><0.05, which was estimated from 3 independent experiments with student <i>t</i>-test in a two-tailed fashion.</p

GAS2 is up-regulated in chronic myeloid leukemia.

<p>(A) The cells from chronic myeloid leukemia (CML) patients and healthy donors were collected and processed to yield nucleated cells, and then CD34⁺ cells were enriched with immunomagnetic method. The gene expression of <i>GAS2</i> was assessed with nucleated cells and CD34⁺ cells, respectively. For the nucleated cells, 7 healthy donors and 25 CML patients were recruited; for the CD34⁺ cells, 3 healthy donors and 8 CML patients were recruited. (B) Immunofluorescence assay was used to detect the expression of GAS2 (green) in CD34⁺ cells of normal bone marrow (NBM) and CML patient, together with K562 and MEG-01 cells. Hoechst 33342 was used to visualize the nuclei (blue). The representative graphs of individual section with confocal microscopy analyses were shown. Original magnification: ×150. The data were shown as mean ± S.E.M.; **mean <i>p</i><0.01, which was estimated with student <i>t</i>-test in a two-tailed fashion.</p

Targeting GAS2 with both RNAi and GAS2DN inhibits K562 cells.

<p>(A) The expression of <i>GAS2</i> was measured in shNC (control), shGAS2#1 and shGAS2#2 transduced cells with Q-RT-PCR. (B) The expression of GAS2 protein in shNC, shGAS2#1 and shGAS2#2 transduced cells were analyzed with FACS. (C) Calpain activities of shNC, shGAS2#1 and shGAS2#2 transduced cells were measured. (D) & (E) The proliferation and colony-forming cell (CFC) capacities of various virally transduced K562 cells were measured. (F) The schematic structure of lentiviral vector to express GAS2DN. LTR, long terminal repeat; pSFFV, spleen focus forming virus promoter; IRES, internal ribosome entry site; Venus, the enhanced yellow fluorescent protein; GAS2DN, the dominant negative form of GAS2. (G) The western blot was used to detect the expression of GAS2DN with an antibody recognizing N-terminus of GAS2 (N-GAS2). (H) Calpain activities of Venus and GAS2DN transduced cells were measured. (I) K562 cells were transduced with various lentiviral vectors. The FACS purified cells were plated in methylcellulose media with Imatinib mesylate (IM, final concentration as 1 µM and 2 µM), and then the colonies were numerated. The survival rates of CFC (IM treated versus IM untreated) of various transduced cells were calculated and compared. The data were shown as mean ± S.E.M. from at least 3 independent experiments; *mean <i>p</i><0.05, which was estimated with student <i>t</i>-test in a two-tailed fashion.</p

The expression and activity of beta-catenin are not affected by GAS2 targeting.

<p>(A) Immunofluorescence assay was used to detect the expression of beta-catenin (red) in Venus and GAS2DN transduced cells with Hoechst 33342 to visualize the nuclei (blue). The representative graphs of individual section with confocal microscopy analyses were shown. (B) The expression of beta-catenin was assessed quantitatively with flow cytometry in various virally transduced K562 cells. (C) The transcription activity of beta-catenin was measured in SW620 (human colorectal adenocarcinoma cell, as a positive control of beta-catenin activity), K562 and MEG-01 cells (upper panel), Venus and GAS2DN transduced K562 and MEG-01 cells (middle panel), and shNC, shGAS2#1 and shGAS2#2 transduced K562 cells (lower panel). The beta-catenin activated reporter vector (TOPflash) or its mutant vector (FOPflash) plus renilla reporter vector were used for transfection. The ratio of normalized TOPflash versus FOPflash was used to represent the activity of beta-catenin. (D) The cytosol and nucleus protein from various virally transduced cells were purified, and then subjected to immunoblot with antibodies against Tublin, beta-catenin and histone H3, respectively. The representative graph of 3 independent experiments was displayed.</p

Growth Arrest Specific 2 Is Up-Regulated in Chronic Myeloid Leukemia Cells and Required for Their Growth

<div><p>Although the generation of BCR-ABL is the molecular hallmark of chronic myeloid leukemia (CML), the comprehensive molecular mechanisms of the disease remain unclear yet. Growth arrest specific 2 (GAS2) regulates multiple cellular functions including cell cycle, apoptosis and calpain activities. In the present study, we found GAS2 was up-regulated in CML cells including CD34⁺ progenitor cells compared to their normal counterparts. We utilized RNAi and the expression of dominant negative form of GAS2 (GAS2DN) to target GAS2, which resulted in calpain activity enhancement and growth inhibition of both K562 and MEG-01 cells. Targeting GAS2 also sensitized K562 cells to Imatinib mesylate (IM). GAS2DN suppressed the tumorigenic ability of MEG-01 cells and impaired the tumour growth as well. Moreover, the CD34⁺ cells from CML patients and healthy donors were transduced with control and GAS2DN lentiviral vectors, and the CD34⁺ transduced (YFP⁺) progeny cells (CD34⁺YFP⁺) were plated for colony-forming cell (CFC) assay. The results showed that GAS2DN inhibited the CFC production of CML cells by 57±3% (n = 3), while affected those of normal hematopoietic cells by 31±1% (n = 2). Next, we found the inhibition of CML cells by GAS2DN was dependent on calpain activity but not the degradation of beta-catenin. Lastly, we generated microarray data to identify the differentially expressed genes upon GAS2DN and validated that the expression of <i>HNRPDL</i>, <i>PTK7</i> and <i>UCHL5</i> was suppressed by GAS2DN. These 3 genes were up-regulated in CML cells compared to normal control cells and the growth of K562 cells was inhibited upon HNRPDL silence. Taken together, we have demonstrated that GAS2 is up-regulated in CML cells and the inhibition of GAS2 impairs the growth of CML cells, which indicates GAS2 is a novel regulator of CML cells and a potential therapeutic target of this disease.</p></div