We report a new hybrid integration scheme that offers for the first time a nanowire-on-lead approach, which enables independent electrical addressability, is scalable, and has superior spatial resolution in vertical nanowire arrays. The fabrication of these nanowire arrays is demonstrated to be scalable down to submicrometer site-to-site spacing and can be combined with standard integrated circuit fabrication technologies. We utilize these arrays to perform electrophysiological recordings from mouse and rat primary neurons and human induced pluripotent stem cell (hiPSC)-derived neurons, which revealed high signal-to-noise ratios and sensitivity to subthreshold postsynaptic potentials (PSPs). We measured electrical activity from rodent neurons from 8 days in vitro (DIV) to 14 DIV and from hiPSC-derived neurons at 6 weeks in vitro post culture with signal amplitudes up to 99 mV. Overall, our platform paves the way for longitudinal electrophysiological experiments on synaptic activity in human iPSC based disease models of neuronal networks, critical for understanding the mechanisms of neurological diseases and for developing drugs to treat them.

Reduced contact size would permit higher resolution cortical recordings, but the effects of diameter on crucial recording and stimulation properties are poorly understood. Here, the first systematic study of scaling effects on the electrochemical properties of metallic Pt and Au and organic poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) electrodes is presented. PEDOT:PSS exhibits better faradaic charge transfer and capacitive charge coupling than metal electrodes, and these characteristics lead to improved electrochemical performance and reduced noise at smaller electrode diameters. PEDOT:PSS coating reduces the impedances of metallic electrodes by up to 18x for diameters <200 µm, but has no effect for millimeter scale contacts due to the dominance of series resistances. Therefore, the performance gains are especially significant at smaller diameters and lower frequencies essential for recording cognitive and pathological activities. Additionally, the overall reduced noise of the PEDOT:PSS electrodes enables a lower noise floor for recording action potentials. These results permit quantitative optimization of contact material and diameter for different electrocorticography applications.

No abstract

When receiving machine learning services from the cloud, the provider does not need to receive all features; in fact, only a subset of the features are necessary for the target prediction task. Discerning this subset is the key problem of this work. We formulate this problem as a gradient-based perturbation maximization method that discovers this subset in the input feature space with respect to the functionality of the prediction model used by the provider. After identifying the subset, our framework, Cloak, suppresses the rest of the features using utility-preserving constant values that are discovered through a separate gradient-based optimization process. We show that Cloak does not necessarily require collaboration from the service provider beyond its normal service, and can be applied in scenarios where we only have black-box access to the service provider's model. We theoretically guarantee that Cloak's optimizations reduce the upper bound of the Mutual Information (MI) between the data and the sifted representations that are sent out. Experimental results show that Cloak reduces the mutual information between the input and the sifted representations by 85.01% with only negligible reduction in utility (1.42%). In addition, we show that Cloak greatly diminishes adversaries' ability to learn and infer non-conducive features. CCS CONCEPTS• Security and privacy → Privacy protections; Usability in security and privacy; • Computing methodologies → Neural networks; Computer vision tasks; • Mathematics of computing → Information theory.

Deep quantization of neural networks (below eight bits) offers significant promise in reducing their compute and storage cost. Albeit alluring, without special techniques for training and optimization, deep quantization results in significant accuracy loss. To further mitigate this loss, we propose a novel sinusoidal regularization, called SinReQ, for deep quantized training. SinReQ adds a periodic term to the original objective function of the underlying training algorithm. SinReQ exploits the periodicity, differentiability, and the desired convexity profile in sinusoidal functions to automatically propel weights towards values that are inherently closer to quantization levels. Since, this technique does not require invasive changes to the training procedure, SinReQ can harmoniously enhance quantized training algorithms. SinReQ offers generality and flexibility as it is not limited to a certain bitwidth or a uniform assignment of bitwidths across layers. We carry out experimentation using the CIFAR-10, ResNet-20, SVHN DNNs with three to five bits for quantization and show the versatility of SinReQ in enhancing multiple quantized training algorithms, DoReFa (Zhou et al., 2016) and WRPN (Mishra et al., 2018). Averaging across all the bit configurations shows that SinReQ closes the accuracy gap between these two techniques and the full-precision runs by 35.7% and 37.1%, respectively. That is improving the absolute accuracy of DoReFa and WRPN up to 5.3% and 2.6%, respectively.

Nanoscale trigate FinFET with channel lengths down to 9.7 nm as projected by the 2013 International Technology Roadmap of Semiconductors (ITRS-2013) are simulated by means of quantum corrected 3-D Monte Carlo technique in the ballistic and quasi-ballistic regimes. Ballisticity ratio (BR) is extracted and found to reach values as high as 90% at L G = 9.7 nm. The impact of the ITRS-2013 scaling strategy on the BR, and ON-/ OFF-states is discussed. Forward and backward electron velocity components are extracted along the channel to analyze the electron transport in detail. Velocity profile is found to be characterized by two critical points along the channel, each is associated with a change in the electron acceleration showing the physical significance of the off-equilibrium transport with scaling the channel length. Index Terms-3-D Monte Carlo (MC) technique, ballisticity ratio (BR), forward/backward electron velocities, trigate (TG) FinFET.

As deep neural networks make their ways into different domains and application, their compute efficiency is becoming a first-order constraint. Deep quantization, which reduces the bitwidth of the operations (below eight bits), offers a unique opportunity as it can reduce both the storage and compute requirements of the network superlinearly. However, if not employed with diligence, this can lead to significant accuracy loss. Due to the strong inter-dependence between layers and exhibiting different characteristics across the same network, choosing an optimal bitwidth per layer granularity is not a straight forward. As such, deep quantization opens a large hyper-parameter space, the exploration of which is a major challenge. We propose a novel sinusoidal regularization, called SINAREQ, for deep quantized training. Leveraging the sinusoidal properties, we seek to learn multiple quantization parameterization in conjunction during gradient-based training process. Specifically, we learn (i) a per-layer quantization bitwidth along with (ii) a scale factor through learning the period of the sinusoidal function. At the same time, we exploit the periodicity, differentiability, and the local convexity profile in sinusoidal functions to automatically propel (iii) network weights towards values quantized at levels that are jointly determined. We show how SINAREQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet,

No abstract