Fast and robust analog in-memory deep neural network training.

Analog in-memory computing is a promising future technology for efficiently accelerating deep learning networks. While using in-memory computing to accelerate the inference phase has been studied extensively, accelerating the training phase has received less attention, despite its arguably much larger compute demand to accelerate. While some analog in-memory training algorithms have been suggested, they either invoke significant amount of auxiliary digital compute-accumulating the gradient in digital floating point precision, limiting the potential speed-up-or suffer from the need for near perfectly programming reference conductance values to establish an algorithmic zero point.

Exploring TNFi drug-levels and anti-drug antibodies during tapering among patients with inflammatory arthritis: secondary analyses from the randomised BIODOPT trial.

To evaluate tumour necrosis factor inhibitor (TNFi) drug-levels and presence of anti-drug antibodies (ADAb) in patients with inflammatory arthritis who taper TNFi compared to TNFi continuation. Patients with rheumatoid arthritis, psoriatic arthritis, or axial spondyloarthritis on stable TNFi dose and in low disease activity ≥ 12 months were randomised (2:1) to disease activity-guided tapering or control. Blood samples at baseline, 12- and 18-months were evaluated for TNFi drug-levels and ADAb.

Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm.

A critical bottleneck for the training of large neural networks (NNs) is communication with off-chip memory. A promising mitigation effort consists of integrating crossbar arrays of analogue memories in the Back-End-Of-Line, to store the NN parameters and efficiently perform the required synaptic operations. The "" algorithm was developed to facilitate NN training in the presence of device nonidealities.