ClaPIM: Scalable Sequence CLAssification using Processing-In-Memory

DNA sequence classification is a fundamental task in computational biology with vast implications for applications such as disease prevention and drug design. Therefore, fast high-quality sequence classifiers are significantly important. This paper introduces ClaPIM, a scalable DNA sequence classification architecture based on the emerging concept of hybrid in-crossbar and near-crossbar memristive processing-in-memory (PIM). We enable efficient and high-quality classification by uniting the filter and search stages within a single algorithm. Specifically, we propose a custom filtering technique that drastically narrows the search space and a search approach that facilitates approximate string matching through a distance function. ClaPIM is the first PIM architecture for scalable approximate string matching that benefits from the high density of memristive crossbar arrays and the massive computational parallelism of PIM. Compared with Kraken2, a state-of-the-art software classifier, ClaPIM provides significantly higher classification quality (up to 20x improvement in F1 score) and also demonstrates a 1.8x throughput improvement. Compared with EDAM, a recently-proposed SRAM-based accelerator that is restricted to small datasets, we observe both a 30.4x improvement in normalized throughput per area and a 7% increase in classification precision

MTJ-Based Hardware Synapse Design for Quantized Deep Neural Networks

Quantized neural networks (QNNs) are being actively researched as a solution for the computational complexity and memory intensity of deep neural networks. This has sparked efforts to develop algorithms that support both inference and training with quantized weight and activation values without sacrificing accuracy. A recent example is the GXNOR framework for stochastic training of ternary and binary neural networks. In this paper, we introduce a novel hardware synapse circuit that uses magnetic tunnel junction (MTJ) devices to support the GXNOR training. Our solution enables processing near memory (PNM) of QNNs, therefore can further reduce the data movements from and into the memory. We simulated MTJ-based stochastic training of a TNN over the MNIST and SVHN datasets and achieved an accuracy of 98.61% and 93.99%, respectively

Predictors of loneliness during the Covid-19 pandemic in people with dementia and their carers in England: findings from the DETERMIND-C19 study

Objectives To identify factors that predict the risk of loneliness for people with dementia and carers during a pandemic. Methods People with dementia and their carers completed assessments before (July 2019–March 2020; 206 dyads) and after (July–October 2020) the first Covid-19 ‘lockdown’ in England. At follow-up, the analytic sample comprised 67 people with dementia and 108 carers. We built a longitudinal path model with loneliness as an observed outcome. Carer type and social contacts at both measurements were considered. Other social resources (quality of relationship, formal day activities), wellbeing (anxiety, psychological wellbeing) and cognitive impairment were measured with initial level and change using latent growth curves. We adjusted for socio-demographic factors and health at baseline. Results In carers, higher levels of loneliness were directly associated with non-spouse coresident carer type, level and increase of anxiety in carer, more formal day activities, and higher cognitive impairment in the person with dementia. In people with dementia, non-spouse coresident carer type, and higher initial levels of social resources, wellbeing, and cognitive impairment predicted the changes in these factors; this produced indirect effects on social contacts and loneliness. Conclusion Loneliness in the Covid-19 pandemic appears to be shaped by different mechanisms for people with dementia and their carers. The results suggest that carers of those with dementia may prioritize providing care that protects the person with dementia from loneliness at the cost of experiencing loneliness themselves. Directions for the promotion of adaptive social care during the Covid-19 pandemic and beyond are discussed

Emotion-Focused Dyadic Coping Styles used by Family Carers of People with Dementia during the COVID-19 Pandemic

Emotional wellbeing of family carers and people with dementia is associated with not only how each individual copes with stress and conflict, but also by how they cope together. Finding ways to positively cope together was particularly important during COVID-19 lockdown restrictions, when other avenues of emotional support were less available. We explored how carers experienced and used emotion-focused dyadic coping styles during the COVID-19 pandemic. In-depth qualitative interviews were conducted during the pandemic with 42 family carers, supplemented by quality of life scores collected both pre- and during the pandemic and household status. Abductive thematic analysis identified five styles of emotion-focused dyadic coping: common, supportive, hostile, disengaged avoidance and protective. The COVID-19 pandemic left many dyads unsupported. While many carers adapted, reporting increases in quality of life and enjoying the extra time with the person with dementia, others experienced dyadic conflict and reductions in quality of life. This variation was associated with dyadic coping styles, including challenges in using ‘positive’ styles and the protective use of ‘negative’ disengaged avoidance in the right situations. Dyadic coping styles also differed as a function of whether the dyad lived together. As many people with dementia are supported by an informal carer, considering how they cope together could help us to better support them. We make suggestions for dyadic interventions tailored by co-residency status that could help dyads identify and communicate coping needs, reconnect following avoidance coping, and replenish their coping resources through social support

Emotion-focused dyadic coping styles used by family carers of people with dementia during the COVID-19 pandemic

Emotional wellbeing of family carers and people with dementia is associated with not only how each individual copes with stress and conflict, but also by how they cope together. Finding ways to positively cope together was particularly important during COVID-19 lockdown restrictions, when other avenues of emotional support were less available. We explored how carers experienced and used emotion-focused dyadic coping styles during the COVID-19 pandemic. In-depth qualitative interviews were conducted during the pandemic with 42 family carers, supplemented by quality of life scores collected both pre- and during the pandemic and household status. Abductive thematic analysis identified five styles of emotion-focused dyadic coping: common, supportive, hostile, disengaged avoidance and protective. The COVID-19 pandemic left many dyads unsupported. While many carers adapted, reporting increases in quality of life and enjoying the extra time with the person with dementia, others experienced dyadic conflict and reductions in quality of life. This variation was associated with dyadic coping styles, including challenges in using 'positive' styles and the protective use of 'negative' disengaged avoidance in the right situations. Dyadic coping styles also differed as a function of whether the dyad lived together. As many people with dementia are supported by an informal carer, considering how they cope together could help us to better support them. We make suggestions for dyadic interventions tailored by co-residency status that could help dyads identify and communicate coping needs, reconnect following avoidance coping, and replenish their coping resources through social support

Using digital technologies to facilitate social inclusion during the COVID-19 pandemic: experiences of co-resident and non-co-resident family carers of people with dementia from DETERMIND-C19

Background The COVID-19 pandemic triggered rapid and unprecedented changes in the use of digital technologies to support people's social inclusion. We examined whether and how co-resident and non-co-resident family carers of people with dementia engaged with digital technologies during this period. Methods Throughout November 2020-February 2021, we interviewed 42 family carers of people with dementia from our DETERMIND-C19 cohort. Preliminary analysis was conducted through Framework analysis, followed by an inductive thematic analysis. Findings Digital technologies served as a Facilitator for social inclusion by enabling carers to counter the effects of the differing restrictions imposed on them so they could remain socially connected and form a sense of solidarity, access resources and information, engage in social and cultural activities and provide support and independence in their caring role. However, these experiences were not universal as carers discussed some Challenges for tech inclusion, which included preferences for face-to-face contact, lack of technological literacy and issues associated with the accessibility of the technology. Conclusion Many of the carers engaged with Information and Communication Technologies, and to a lesser extent Assistive Technologies, during the pandemic. Whilst carers experienced different challenges due to where they lived, broadly the use of these devices helped them realise important facets of social inclusion as well as facilitated the support they provided to the person with dementia. However, to reduce the ‘digital divide’ and support the social inclusion of all dementia carers, our findings suggest it is essential that services are attuned to their preferences, needs and technological abilities

PIMDB: Understanding Bulk-Bitwise Processing In-Memory Through Database Analytics

Bulk-bitwise processing-in-memory (PIM), where large bitwise operations are performed in parallel by the memory array itself, is an emerging form of computation with the potential to mitigate the memory wall problem. This paper examines the capabilities of bulk-bitwise PIM by constructing PIMDB, a fully-digital system based on memristive stateful logic, utilizing and focusing on in-memory bulk-bitwise operations, designed to accelerate a real-life workload: analytical processing of relational databases. We introduce a host processor programming model to support bulk-bitwise PIM in virtual memory, develop techniques to efficiently perform in-memory filtering and aggregation operations, and present the mapping of the application into the memory. When tested against an equivalent in-memory database on the same host system, our approach substantially lowers the number of required memory read operations, thus accelerating TPC-H filter operations by 1.6$\times$--18$\times$ and full queries by 56$\times$--608$\times$, while reducing the energy consumption by 1.7$\times$--18.6$\times$ and 0.81$\times$--12$\times$ for these benchmarks, respectively. Our extensive evaluation uses the gem5 full-system simulation environment