Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.

Analog in-memory computing-a promising approach for energy-efficient acceleration of deep learning workloads-computes matrix-vector multiplications but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable inference accuracy. Here, we develop an hardware-aware retraining approach to systematically examine the accuracy of analog in-memory computing across multiple network topologies, and investigate sensitivity and robustness to a broad set of nonidealities.

Optimised weight programming for analogue memory-based deep neural networks.

Analogue memory-based deep neural networks provide energy-efficiency and per-area throughput gains relative to state-of-the-art digital counterparts such as graphics processing units. Recent advances focus largely on hardware-aware algorithmic training and improvements to circuits, architectures, and memory devices. Optimal translation of software-trained weights into analogue hardware weights-given the plethora of complex memory non-idealities-represents an equally important task.

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices.

Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential of Analog AI accelerators based on Non-Volatile Memory, in particular Phase Change Memory (PCM), for software-equivalent accurate inference of natural language processing applications.

Training fully connected networks with resistive memories: impact of device failures.

Hardware accelerators based on two-terminal non-volatile memories (NVMs) can potentially provide competitive speed and accuracy for the training of fully connected deep neural networks (FC-DNNs), with respect to GPUs and other digital accelerators. We recently proposed [S. Ambrogio et al.

Equivalent-accuracy accelerated neural-network training using analogue memory.

Neural-network training can be slow and energy intensive, owing to the need to transfer the weight data for the network between conventional digital memory chips and processor chips. Analogue non-volatile memory can accelerate the neural-network training algorithm known as backpropagation by performing parallelized multiply-accumulate operations in the analogue domain at the location of the weight data. However, the classification accuracies of such in situ training using non-volatile-memory hardware have generally been less than those of software-based training, owing to insufficient dynamic range and excessive weight-update asymmetry.

Light funneling from a photonic crystal laser cavity to a nano-antenna: overcoming the diffraction limit in optical energy transfer down to the nanoscale.

We show that the near-field coupling between a photonic crystal microlaser and a nano-antenna can enable hybrid photonic systems that are both physically compact (free from bulky optics) and efficient at transferring optical energy into the nano-antenna. Up to 19% of the laser power from a micron-scale photonic crystal laser cavity is experimentally transferred to a bowtie aperture nano-antenna (BNA) whose area is 400-fold smaller than the overall emission area of the microlaser. Instead of a direct deposition of the nano-antenna onto the photonic crystal, it is fabricated at the apex of a fiber tip to be accurately placed in the microlaser near-field.

Observation of the role of subcritical nuclei in crystallization of a glassy solid.

Phase transformation generally begins with nucleation, in which a small aggregate of atoms organizes into a different structural symmetry. The thermodynamic driving forces and kinetic rates have been predicted by classical nucleation theory, but observation of nanometer-scale nuclei has not been possible, except on exposed surfaces. We used a statistical technique called fluctuation transmission electron microscopy to detect nuclei embedded in a glassy solid, and we used a laser pump-probe technique to determine the role of these nuclei in crystallization.

Holographic data storage with arbitrarily misaligned data pages.

An improved postprocessing algorithm that can compensate for arbitrary misregistrations between a detector array and the coherent image of a pixelated two-dimensional data page is described. Previously [Opt. Lett.

Experimental demonstration of gray-scale sparse modulation codes in volume holographic storage.

We discuss experimental results of a versatile nonbinary modulation and channel code appropriatefor two-dimentional page-oriented holographic memories. An enumerative permutation code is used to provide a modulation code that permits a simple maximum-likelihood detection scheme. Experimental results from the IBM Demon testbed are used to characterize the performance and feasibility of the proposed modulation and channel codes.

Density implications of shift compensation postprocessing in holographic storage systems.

We investigate the effect of data page misregistration, and its subsequent correction in postprocessing, on the storage density of holographic data storage systems. A numerical simulation is used to obtain the bit-error rate as a function of hologram aperture, page misregistration, pixel fill factors, and Gaussian additive intensity noise. Postprocessing of simulated data pages is performed by a nonlinear pixel shift compensation algorithm [Opt.