Charge-domain compute-in-memory (CIM) SRAMs have recently become an enticing compromise between computing efficiency and accuracy to process sub-8b convolutional neural networks (CNNs) at the edge. Yet, they commonly make use of a fixed dot-product (DP) voltage swing, which leads to a loss in effective ADC bits due to data-dependent clipping or truncation effects that waste precious conversion energy and computing accuracy. To overcome this, we present IMAGINE, a workload-adaptive 1-to-8b CIM-CNN accelerator in 22nm FD-SOI. It introduces a 1152×256 end-to-end charge-based macro with a multi-bit DP based on an input-serial, weight-parallel accumulation that avoids power-hungry DACs. An adaptive swing is achieved by combining a channel-wise DP array split with a linear in-ADC implementation of analog batch-normalization (ABN), obtaining a distribution-aware data reshaping. Critical design constraints are relaxed by including the post-silicon equivalent noise within a CIM-aware CNN training framework. Measurement results showcase an 8b system-level energy efficiency of 40TOPS/W at 0.3/0.6V, with competitive accuracies on MNIST and CIFAR-10. Moreover, the peak energy and area efficiencies of the 187kB/mm² macro respectively reach up to 0.15-8POPS/W and 2.6-154TOPS/mm², scaling with the 8-to-1b computing precision. These results exceed previous charge-based designs by 3-to-5× while being the first work to provide linear in-memory rescaling.

Magnetic nanoparticles are being actively explored for diagnostic use in inductive biosensors, due to their high susceptibility and low hysteresis. In this work, we demonstrate how drying particles under a DC magnetic field within a membrane substrate affects sensitivity of the sensing platform. When the nanoparticles (Fe3O4, CoFe2O4, MnFe2O4) are dried within a static magnetic field parallel to the inductive AC sensing field, the sensor response is increased by 6 to 16 % depending on sample alloy and mass. Furthermore, when the drying field and excitation field are orthogonal, the signal is decreased by similar factors. The results point to a Brownian rotation of the particles during the drying process.

Recent advances in artificial intelligence (AI) have prompted the search for enhanced algorithms and hardware to support the deployment of machine learning (ML) at the edge. More specifically, in the context of the Internet of Things (IoT), vision chips must be able to fulfill the tasks of low to medium complexity, such as feature extraction (FE) or region-of-interest (RoI) detection, with a sub-mW power budget imposed by the use of small batteries or energy harvesting. Mixed-signal vision chips relying on in-or near-sensor processing have emerged as an interesting candidate because of their favorable tradeoff between energy efficiency (EE) and computational accuracy compared with digital systems for these specific tasks. In this article, we introduce a mixed-signal convolutional imager system-on-chip (SoC) codenamed MANTIS, featuring a unique combination of large 16 × 16 4b-weighted filters, operation at multiple scales, and double sampling, well suited to the requirements of medium-complexity tasks. The main contributions are (i) circuits called DS3 units combining delta-reset sampling (DRS), image downsampling (DS), and voltage downshifting and (ii) charge-domain multiply-and-accumulate (MAC) operations based on switched-capacitor (SC) amplifiers and charge sharing in the capacitive DAC of the successive-approximation (SAR) ADCs, MANTIS achieves peak EEs normalized to 1b operations of 4.6 and 84.1 TOPS/W at the accelerator and SoC levels, while computing feature maps (fmaps) with a root-mean-square error (RMSE) ranging from 3 to 11.3%. It also demonstrates a face RoI detection with a false negative rate (FNR) of 11.5%, while discarding 81.3% of image patches and reducing the data transmitted off chip by 13 × compared with the raw image.