|
Artificial Intelligence
Machine Learning
Data-intensive computing
Abstract
USC researchers propose a method called Augmented Memory Computing, which allows an SRAM cell to (1) function like a standard 6 transistor (6T) SRAM cell, storing one bit of data in static format, or (2) function in an augmented mode, storing more than one bit of data in...
|
|
Artificial intelligence
Encryption
Neuromorphic computing
Abstract
USC inventors have designed a hybrid memory array with optical and magnetic components enabling ultra-fast read and write operations, called NEBULA. NEBULA is a wafer-scale single super non-volatile magnetic memory chip with optical read and write capability and optical interconnects....
|
|
Quantum computing
Cryogenic quantum chip to classical world quantum interconnect for heterogeneous quantum devices (memory, sensors, detectors, circulators, qubit sources)
Abstract
USC inventors have designed a heterogeneously integrated quantum chip optoelectronics interposer (QuIP). QuIP enhances qubit performance, offers controlled coupling between...
|
|
Autonomous vehicles
Robotics
Surveillance and security
Abstract
USC researchers bring advanced retinal computations to image sensors with Integrated Retinal Functionality in Image Sensors (IRIS). By leveraging recent advancements in inner retinal circuits, IRIS goes beyond basic luminance adaptation and change detection. It mimics the feature-selective...
|
|
COVID-19 detection
Viral detection
Cancer marker detection
Abstract
USC inventors propose a novel selectively-sensing bio-photonics microfluidic optical ring resonator-based integrated chip architecture with an on-chip spectrometer consisting of coupled ring resonator filters and integrated photodetector arrays for detection of COVID-19. The design...
|
|
Integrated circuits
Abstract
USC inventors propose an analog optical interconnect between the CMOS image sensor and the memory/processor that enables significant bandwidth reduction and promotes energy efficiency. This method would both remove the need for for ADCs between transceiver nodes and allow for analog in-memory/pixel computation, resulting...
|
|
Computing
Neural networks
Artificial Intelligence
Abstract
USC researchers propose a novel edge intelligence computing paradigm that employs in-pixel, massively parallel analog computing. The system utilizes monolithically or heterogeneously integrated memory and image sensors based on multi-bit, multi-channel memory embedded pixels. This paradigm...
|
|
Autonomous vehicles
Disaster management
Wildlife monitoring
Abstract
USC researchers present a novel processing-in-pixel-in-memory paradigm. This approach enhances the pixel array with analog multi-channel, multi-bit convolution, batch normalization, and Rectified Linear Units support. By adopting a holistic algorithm-circuit design strategy, the...
|
|
Autonomous driving
Surveillance
Object detection; Object tracking; Anomaly detection
Abstract
USC researchers have developed an asynchronous processing-in-pixel-in-memory (P2M) paradigm that improves energy efficiency and decreases processing requirements and data transfer differences without sacrificing significant accuracy. The P2M paradigm consumes...
|