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.

An ELIME assay for hepatitis A virus detection.

An Enzyme Linked ImmunoMagnetic Electrochemical assay (ELIME) was developed for the detection of the hepatitis A virus (HAV). This system is based on the use of new polydopamine-modified magnetic nanobeads as solid support for the immunochemical chain, and an array of 8 screen-printed electrodes as a sensing platform. Enzymatic-by-product is quickly measured by differential pulse voltammetry.

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.

Self-aligned coupled nanowire transistor.

The integration of multiple functionalities into individual nanoelectronic components is increasingly explored as a means to step up computational power, or for advanced signal processing. Here, we report the fabrication of a coupled nanowire transistor, a device where two superimposed high-performance nanowire field-effect transistors capable of mutual interaction form a thyristor-like circuit. The structure embeds an internal level of signal processing, showing promise for applications in analogue computation.

Top-gated silicon nanowire transistors in a single fabrication step.

Top-gated silicon nanowire transistors are fabricated by preparing all terminals (source, drain, and gate) on top of the nanowire in a single step via dose-modulated e-beam lithography. This outperforms other time-consuming approaches requiring alignment of multiple patterns, where alignment tolerances impose a limit on device scaling. We use as gate dielectric the 10-15 nm SiO(2) shell naturally formed during vapor-transport growth of Si nanowires, so the wires can be implemented into devices after synthesis without additional processing.

Ion beam doping of silicon nanowires.

We demonstrate n- and p-type field-effect transistors based on Si nanowires (SiNWs) implanted with P and B at fluences as high as 10(15) cm (-2). Contrary to what would happen in bulk Si for similar fluences, in SiNWs this only induces a limited amount of amorphization and structural disorder, as shown by electrical transport and Raman measurements. We demonstrate that a fully crystalline structure can be recovered by thermal annealing at 800 degrees C.

Nanowire lithography on silicon.

Nanowire lithography (NWL) uses nanowires (NWs), grown and assembled by chemical methods, as etch masks to transfer their one-dimensional morphology to an underlying substrate. Here, we show that SiO2 NWs are a simple and compatible system to implement NWL on crystalline silicon and fabricate a wide range of architectures and devices. Planar field-effect transistors made of a single SOI-NW channel exhibit a contact resistance below 20 kOmega and scale with the channel width.