Ultra-low power logic in memory with commercial grade memristors and FPGA-based smart-IMPLY architecture

Reducing power consumption in nowadays computer technologies represents an increasingly difficult challenge. Conventional computing architectures suffer from the so-called von Neumann bottleneck (VNB), which consists in the continuous need to exchange data and instructions between the memory and the processing unit, leading to significant and apparently unavoidable power consumption. Even the hardware typically employed to run Artificial Intelligence (AI) algorithms, such as Deep Neural Networks (DNN), suffers from this limitation. A change of paradigm is so needed to comply with the ever-increasing demand for ultra-low power, autonomous, and intelligent systems. From this perspective, emerging memristive non-volatile memories are considered a good candidate to lead this technological transition toward the next-generation hardware platforms, enabling the possibility to store and process information in the same place, therefore bypassing the VNB. To evaluate the state of current public-available devices, in this work commercial-grade packaged Self Directed Channel memristors are thoroughly studied to evaluate their performance in the framework of in-memory computing. Specifically, the operating conditions allowing both analog update of the synaptic weight and stable binary switching are identified, along with the associated issues. To this purpose, a dedicated yet prototypical system based on an FPGA control platform is designed and realized. Then, it is exploited to fully characterize the performance in terms of power consumption of an innovative Smart IMPLY (SIMPLY) Logic-in-Memory (LiM) computing framework that allows reliable in-memory computation of classical Boolean operations. The projection of these results to the nanoseconds regime leads to an estimation of the real potential of this computing paradigm. Although not investigated in this work, the presented platform can also be exploited to test memristor-based SNN and Binarized DNNs (i.e., BNN), that can be combined with LiM to provide the heterogeneous flexible architecture envisioned as the long-term goal for ubiquitous and pervasive AI

Standards for the Characterization of Endurance in Resistive Switching Devices

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products

The potential to encode sex, age, and individual identity in the alarm calls of three species of Marmotinae

In addition to encoding referential information and information about the sender’s motivation, mammalian alarm calls may encode information about other attributes of the sender, providing the potential for recognition among kin, mates, and neighbors. Here, we examined 96 speckled ground squirrels (Spermophilus suslicus), 100 yellow ground squirrels (Spermophilus fulvus) and 85 yellow-bellied marmots (Marmota flaviventris) to determine whether their alarm calls differed between species in their ability to encode information about the caller’s sex, age, and identity. Alarm calls were elicited by approaching individually identified animals in live-traps. We assume this experimental design modeled a naturally occurring predatory event, when receivers should acquire information about attributes of a caller from a single bout of alarm calls. In each species, variation that allows identification of the caller’s identity was greater than variation allowing identification of age or sex. We discuss these results in relation to each species’ biology and sociality

Microenvironmental Modulation of Decorin and Lumican in Temozolomide-Resistant Glioblastoma and Neuroblastoma Cancer Stem-Like Cells

The presence of cancer stem cells (CSCs) or tumor-initiating cells can lead to cancer recurrence in a permissive cell–microenvironment interplay, promoting invasion in glioblastoma (GBM) and neuroblastoma (NB). Extracellular matrix (ECM) small leucine-rich proteoglycans (SLRPs) play multiple roles in tissue homeostasis by remodeling the extracellular matrix (ECM) components and modulating intracellular signaling pathways. Due to their pan-inhibitory properties against receptor tyrosine kinases (RTKs), SLRPs are reported to exert anticancer effects in vitro and in vivo. However, their roles seem to be tissue-specific and they are also involved in cancer cell migration and drug resistance, paving the way to complex different scenarios. The aim of this study was to determine whether the SLRPs decorin (DCN) and lumican (LUM) are recruited in cell plasticity and microenvironmental adaptation of differentiated cancer cells induced towards stem-like phenotype. Floating neurospheres were generated by applying CSC enrichment medium (neural stem cell serum-free medium, NSC SFM) to the established SF-268 and SK-N-SH cancer cell lines, cellular models of GBM and NB, respectively. In both models, the time-dependent synergistic activation of DCN and LUM was observed. The highest DCN and LUM mRNA/protein expression was detected after cell exposure to NSC SFM for 8/12 days, considering these cells as SLRP-expressing (SLRP+) CSC-like. Ultrastructural imaging showed the cellular heterogeneity of both the GBM and NB neurospheres and identified the inner living cells. Parental cell lines of both GBM and NB grew only in soft agar + NSC SFM, whereas the secondary neurospheres (originated from SLRP+ t8 CSC-like) showed lower proliferation rates than primary neurospheres. Interestingly, the SLRP+ CSC-like from the GBM and NB neurospheres were resistant to temozolomide (TMZ) at concentrations >750 μM. Our results suggest that GBM and NB CSC-like promote the activation of huge quantities of SLRP in response to CSC enrichment, simultaneously acquiring TMZ resistance, cellular heterogeneity, and a quiescent phenotype, suggesting a novel pivotal role for SLRP in drug resistance and cell plasticity of CSC-like, allowing cell survival and ECM/niche modulation potential.This study was supported by Fundació la Marató TV3, Project n° 111431

Preliminary safety and efficacy of first-line pertuzumab combined with trastuzumab and taxane therapy for HER2-positive locally recurrent or metastatic breast cancer (PERUSE).

BACKGROUND: Pertuzumab combined with trastuzumab and docetaxel is the standard first-line therapy for HER2-positive metastatic breast cancer, based on results from the phase III CLEOPATRA trial. PERUSE was designed to assess the safety and efficacy of investigator-selected taxane with pertuzumab and trastuzumab in this setting. PATIENTS AND METHODS: In the ongoing multicentre single-arm phase IIIb PERUSE study, patients with inoperable HER2-positive advanced breast cancer (locally recurrent/metastatic) (LR/MBC) and no prior systemic therapy for LR/MBC (except endocrine therapy) received docetaxel, paclitaxel or nab-paclitaxel with trastuzumab [8\u2009mg/kg loading dose, then 6\u2009mg/kg every 3\u2009weeks (q3w)] and pertuzumab (840\u2009mg loading dose, then 420\u2009mg q3w) until disease progression or unacceptable toxicity. The primary end point was safety. Secondary end points included overall response rate (ORR) and progression-free survival (PFS). RESULTS: Overall, 1436 patients received at least one treatment dose (initially docetaxel in 775 patients, paclitaxel in 589, nab-paclitaxel in 65; 7 discontinued before starting taxane). Median age was 54\u2009years; 29% had received prior trastuzumab. Median treatment duration was 16\u2009months for pertuzumab and trastuzumab and 4\u2009months for taxane. Compared with docetaxel-containing therapy, paclitaxel-containing therapy was associated with more neuropathy (all-grade peripheral neuropathy 31% versus 16%) but less febrile neutropenia (1% versus 11%) and mucositis (14% versus 25%). At this preliminary analysis (52 months' median follow-up), median PFS was 20.6 [95% confidence interval (CI) 18.9-22.7] months overall (19.6, 23.0 and 18.1\u2009months with docetaxel, paclitaxel and nab-paclitaxel, respectively). ORR was 80% (95% CI 78%-82%) overall (docetaxel 79%, paclitaxel 83%, nab-paclitaxel 77%). CONCLUSIONS: Preliminary findings from PERUSE suggest that the safety and efficacy of first-line pertuzumab, trastuzumab and taxane for HER2-positive LR/MBC are consistent with results from CLEOPATRA. Paclitaxel appears to be a valid alternative taxane backbone to docetaxel, offering similar PFS and ORR with a predictable safety profile. CLINICALTRIALS.GOV: NCT01572038

Comprehensive physics-based RRAM compact model including the effect of variability and multi-level random telegraph noise

Resistive Random Access Memory (RRAM) technologies are a promising candidate for the development of more energy efficient circuits, for computing, security, and storage applications. However, such devices show stochastic behaviours that not only originate from variations introduced during fabrication, but that are intrinsic to their operation. Specifically, cycle-to-cycle variations cause the programmed resistive state to be randomly distributed, while Random Telegraph Noise (RTN) introduces random current fluctuations over time. These phenomena can easily affect the reliability and performance of RRAM-based circuits. Therefore, designing such circuits requires accurate compact models. Although several RRAM compact models have been proposed in the literature, these are rarely implemented following the programming best-practice for improving the simulator convergence, and a compact model that is able to reproduce the device characteristic including thermal effects, RTN, and variability in multiple operating conditions using a single set of parameters is still missing. Also, only a few works in the literature describe the procedure to calibrate such compact models, and even fewer address the calibration of the variability on experimental data. In this work, we extend the UniMORE RRAM physics-based compact model by developing and validating two variability models, (i) a comprehensive variability model which can reproduce the effect of cycle-to-cycle variability in multiple operating conditions, and (ii) a simplified version that requires fewer calibration data and enables to reproduce cycle-to-cycle variations in specific operating conditions. The model is implemented following Verilog-A programming best-practices and validated on data from three RRAM technologies from the literature and experimentally on TiN/Ti/HfOx/TiN devices, and the relation between experimental data and the variability model parameters is described

Self-Heating Effect in Silicon-Germanium Heterostructure Bipolar Transistors in Stress and Operating Conditions

In recent times many systems in a wide range of application fields (e.g., health, material science, security, and communications) exploit the mm-and sub-mm-wave spectrum, which dramatically sped up the growth of the BiCMOS technology integrating silicon germanium (SiGe) heterojunction bipolar transistors (HBTs) and passives. Today, the reliability of such devices is of primary concern, and particular attention is given to the device self-heating (SH), the importance of which is supposed to increase with the device scaling. In this work we develop a TCAD model for SiGe HBT devices that is used to investigate the SH effects in SiGe HBTs both in operating and stress conditions. We underline the different role played by impact ionization and carriers' and lattice heating on the device degradation. Results show the important role played by the back end-of-line (BEOL) and by the substrate thermal resistance in dissipating the heat generated by impact ionization and hot carriers. Simulations of the SH effects in stress conditions excluded annealing as the possible reason for the degradation dynamics reported in the literature, while simulations of stressed devices in measurement conditions revealed the presence of a hole hot spot that suggests a possible physical mechanism involved in the degradation slowdown at long stress times reported in the literature