XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference

Binary Neural Networks (BNNs) are promising to deliver accuracy comparable to conventional deep neural networks at a fraction of the cost in terms of memory and energy. In this paper, we introduce the XNOR Neural Engine (XNE), a fully digital configurable hardware accelerator IP for BNNs, integrated within a microcontroller unit (MCU) equipped with an autonomous I/O subsystem and hybrid SRAM / standard cell memory. The XNE is able to fully compute convolutional and dense layers in autonomy or in cooperation with the core in the MCU to realize more complex behaviors. We show post-synthesis results in 65nm and 22nm technology for the XNE IP and post-layout results in 22nm for the full MCU indicating that this system can drop the energy cost per binary operation to 21.6fJ per operation at 0.4V, and at the same time is flexible and performant enough to execute state-of-the-art BNN topologies such as ResNet-34 in less than 2.2mJ per frame at 8.9 fps.Comment: 11 pages, 8 figures, 2 tables, 3 listings. Accepted for presentation at CODES'18 and for publication in IEEE Transactions on Computer-Aided Design of Circuits and Systems (TCAD) as part of the ESWEEK-TCAD special issu

Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70$\pm$15.90% (mean$\pm$ standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6$\pm$14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27$\pm$15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47$\pm$12.8% accuracy with 1.6x faster testing than CSP.Comment: Published as a conference paper at the IEEE European Signal Processing Conference (EUSIPCO), 201

Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node

Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise, making aggressive voltage scaling attractive as a power-saving technique for both logic and SRAMs. In this work, we introduce the first fully programmable IoT end-node system-on-chip (SoC) capable of executing software-defined, hardware-accelerated BNNs at ultra-low voltage. Our SoC exploits a hybrid memory scheme where error-vulnerable SRAMs are complemented by reliable standard-cell memories to safely store critical data under aggressive voltage scaling. On a prototype in 22nm FDX technology, we demonstrate that both the logic and SRAM voltage can be dropped to 0.5Vwithout any accuracy penalty on a BNN trained for the CIFAR-10 dataset, improving energy efficiency by 2.2X w.r.t. nominal conditions. Furthermore, we show that the supply voltage can be dropped to 0.42V (50% of nominal) while keeping more than99% of the nominal accuracy (with a bit error rate ~1/1000). In this operating point, our prototype performs 4Gop/s (15.4Inference/s on the CIFAR-10 dataset) by computing up to 13binary ops per pJ, achieving 22.8 Inference/s/mW while keeping within a peak power envelope of 674uW - low enough to enable always-on operation in ultra-low power smart cameras, long-lifetime environmental sensors, and insect-sized pico-drones.Comment: Submitted to ISICAS2020 journal special issu

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

PrimPol is required for replicative tolerance of G quadruplexes in vertebrate cells

G quadruplexes (G4s) can present potent blocks to DNA replication. Accurate and timely replication of G4s in vertebrates requires multiple specialized DNA helicases and polymerases to prevent genetic and epigenetic instability. Here we report that PrimPol, a recently described primase-polymerase (PrimPol), plays a crucial role in the bypass of leading strand G4 structures. While PrimPol is unable to directly replicate G4s, it can bind and reprime downstream of these structures. Disruption of either the catalytic activity or zinc-finger of PrimPol results in extreme G4-dependent epigenetic instability at the BU-1 locus in avian DT40 cells, indicative of extensive uncoupling of the replicative helicase and polymerase. Together, these observations implicate PrimPol in promoting restart of DNA synthesis downstream of, but closely coupled to, G4 replication impediments

Different patterns of HIV-1 DNA after therapy discontinuation

Background: By persisting in infected cells for a long period of time, proviral HIV-1 DNA can represent an alternative viral marker to RNA viral load during the follow-up of HIV-1 infected individuals. In the present study sequential blood samples of 10 patients under antiretroviral treatment from 1997 with two NRTIs, who refused to continue any antiviral regimen, were analyzed for 16-24 weeks to study the possible relationship between DNA and RNA viral load. Methods: The amount of proviral DNA was quantified by SYBR green real-time PCR in peripheral blood mononuclear cells from a selected group of ten patients with different levels of plasmatic viremia (RNA viral load). Results: Variable levels of proviral DNA were found without any significant correlation between proviral load and plasma HIV-1 RNA levels. Results obtained showed an increase or a rebound in viral DNA in most patients, suggesting that the absence of therapy reflects an increase and/or a persistence of cells containing viral DNA. Conclusion: Even though plasma HIV RNA levels remain the basic parameter to monitor the intensity of viral replication, the results obtained seem to indicate that DNA levels could represent an adjunct prognostic marker in monitoring HIV-1 infected subjects

Sleep Disturbance in Patients with Burning Mouth Syndrome: A Case-Control Study

AIM: To examine sleep complaints in patients with burning mouth syndrome (BMS) and the relationships between these disturbances, negative mood, and pain. METHODS: Fifty BMS patients were compared with an equal number of healthy controls matched for age, sex, and educational level. The Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), the Hamilton Rating Scales for Depression (HAM-D) and Anxiety (HAM-A) were administered. Descriptive statistics, including the Mann-Whitney U test and hierarchical multiple linear regression analyses were used. RESULTS: BMS patients had higher scores in all items of the PSQI and ESS than the healthy controls (P < .001). In the BMS patients, a depressed mood and anxiety correlated positively with sleep disturbances. The Pearson correlations were 0.68 for PSQI vs HAM-D (P < .001) and 0.63 for PSQI vs HAM-A (P < .001). CONCLUSION: BMS patients reported a greater degree of sleep disorders, anxiety, and depression as compared with controls. Sleep disorders could influence quality of life of BMS patients and could be a possible treatment target

The Disturbed Habitat and Its Effects on the Animal Population

Changes in the “habitat” may interfere with the normal functioning of all biological systems. The existence of relationships between environmental changes and health in humans and animal species is well known and it has become generally accepted that poor health affects the animal’s natural behaviors and animal welfare and, consequently, food safety and animal production quality. Microclimate alterations, husbandry-management conditions, quality of human-animal interactions, feeding systems, and rearing environment represent the main factors that could negatively affect animal welfare and may produce behavioral, biochemical, endocrine, and pathological modifications in domestic and wild animals. Particularly, high stress levels can reduce the immune system response and promote infectious diseases. Adverse socio-environmental factors can represent a major stimulus to the development of different pathologies. This chapter will discuss the main pathological modifications described in domestic and wild animals due to “disturbed habitat” paying more attention to critical points detected in standard breeding systems