Automatic Generation of High-Coverage Tests for RTL Designs using Software Techniques and Tools

Register Transfer Level (RTL) design validation is a crucial stage in the hardware design process. We present a new approach to enhancing RTL design validation using available software techniques and tools. Our approach converts the source code of a RTL design into a C++ software program. Then a powerful symbolic execution engine is employed to execute the converted C++ program symbolically to generate test cases. To better generate efficient test cases, we limit the number of cycles to guide symbolic execution. Moreover, we add bit-level symbolic variable support into the symbolic execution engine. Generated test cases are further evaluated by simulating the RTL design to get accurate coverage. We have evaluated the approach on a floating point unit (FPU) design. The preliminary results show that our approach can deliver high-quality tests to achieve high coverage

Electrocaloric effect in La-doped BNT-6BT relaxor ferroelectric ceramics

Relaxor [(Bi1/2Na1/2)0.94Ba0.06](1-1.5x)LaxTiO3 (x = 0, 0.03, 0.06, 0.09) ceramics (La-doped BNT-6BT) with composition close to the morphotropic phase boundary (MPB) were successfully prepared by using the conventional solid state reaction method. All samples present almost a pure perovskite phase with the coexistence of tetragonal and rhombohedral. With the increase of La doping content, the degree of the dielectric relaxor dispersion around the dielectric peak which is close to the room temperature increases, and also the transition temperature of ferroelectric-to-relaxor (TF-R) shifts 120 K towards a lower temperature at x = 0.09. The maximum value of the temperature change (ΔT) of the electrocaloric (EC) effect decreases sharply from 1.1 K at x = 0–0.064 K at x = 0.09. A large positive EC effect (maximum ΔT ~ 0.44 K) in a broad temperature range (~ 90 K) close to room temperature is achieved at x = 0.03, indicating that it is a promising lead-free material for application in solid state cooling system. Moreover, it is found that the Maxwell relationship can be well used to assess the EC effects of the La-doped BNT-6BT ceramics when the operating temperature is higher than that of the TF-R, indicating that these relaxor ceramics would perform as an ergodic

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications