Research Projects

The International Technology Roadmap for Semiconductors (ITRS) report states that by 2022, 70-85% of silicon area will be occupied by on-chip memory due to the intensive computing and memory resources required for Artificial Intelligence (AI) inference processes. Scalable, low-power memory, high bandwidth, and reliability are crucial for applications like general-purpose microcontrollers, automotive, and edge-AI. Deep neural networks (DNN) and convolution neural networks (CNN) implementations rely on vector multiplication or multiply and accumulate (MAC) operations, which consume a significant portion of the total power budget in complex CNN/DNN engines. MAC operations are highly computation-intensive and require large amounts of memory cells and signal processing circuits. In-memory computing (IMC) offers efficient implementation of these operations due to its potential for energy-efficiency and throughput advantages. Energy-efficient MAC operation is essential for modern AI engines. This research aims to implement a novel design for IMC that can execute several arithmetic, logical, and vector operations by directly computing the values stored in bit-cells, delivering precise MAC operations for modern AI engines. The research will address limitations and problems at the software level for hardware implementation purposes, advancing the emerging research area of In-Memory Computing for next-generation computing requirements targeting sophisticated AI applications such as image classification, speech recognition, and autonomous vehicles.