Representation learning for post-mapping (PM) netlists is a critical challenge in Electronic Design Automation (EDA), driven by the diverse and complex nature of modern circuit designs. Existing approaches focus on intermediate representations like And-Inverter Graphs (AIGs), limiting their applicability to post-synthesis stages. We introduce DeepCell, a multiview representation learning framework that integrates structural and functional insights from both PM netlists and AIGs to learn rich, generalizable embeddings. At its core, DeepCell employs the novel Mask Circuit Modeling (MCM) mechanism, which refines PM netlist representations in a self-supervised manner using pretrained AIG encoders. DeepCell sets a new benchmark in PM netlist representation, outperforming existing methods in predictive accuracy and reconstruction fidelity. To validate its efficacy, we apply DeepCell to functional Engineering Change Orders (ECO), achieving significant reductions in patch generation costs and runtime while improving patch quality.

Although outdoor localization is already available to the general public and businesses through the wide spread use of the GPS, it is not supported by low-end phones, requires a direct line of sight to satellites and can drain phone battery quickly. The current fingerprinting solutions can provide high-accuracy localization but are based on the client side. This limits their ubiquitous deployment and accuracy. In this paper, we introduce DeepCell: a provider-side fingerprinting localization system that can provide high accuracy localization for any cell phone. To build its fingerprint, DeepCell leverages the unlabeled cellular measurements recorded by the cellular provider while opportunistically synchronizing with selected client devices to get location labels. The fingerprint is then used to train a deep neural network model that is harnessed for localization. To achieve this goal, DeepCell need to address a number of challenges including using unlabeled data from the provider side, handling noise and sparsity, scaling the data to large areas, and finally providing enough data that is required for training deep models without overhead. Evaluation of DeepCell in a typical realistic environment shows that it can achieve a consistent median accuracy of 29m. This accuracy outperforms the state-of-the-art client-based cellular-based systems by more than 75.4%. In addition, the same accuracy is extended to low-end phones.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Investments in AI-enabled healthcare have exploded over the past few years. But even with belt-tightening in 2022, digital health startups using artificial intelligence (AI) have received a whopping $3 billion in funding. That has left plenty of room for startup AI companies to make their mark in healthtech, biotech and medtech.

Deepcell, a life science company pioneering AI-powered cell classification and isolation for cell biology and translational research, today announced the appointment of Kevin Jacobs as the Vice President of Bioinformatics. Jacobs will be responsible for the company's bioinformatics strategy, implementation and its integration with other areas and into the company's offerings. This appointment is the latest addition to Deepcell's rapidly expanding team of scientists, engineers and computer science experts. Deepcell had acquired $20 million in Series A funding last year. Currently, Deepcell is helping to advance precision medicine by combining advances in AI, cell classification and capture, and single-cell analysis to deliver novel insights through an unprecedented view of cell biology.