UC San Diego

Recent years have witnessed a rapid growth in the amount of generated data, owing to the emergence of Internet of Things (IoT). Processing such huge data on traditional computing systems is highly inefficient, mainly due to the limited cache capacity and memory bandwidth. Processing in-memory (PIM) is an emerging paradigm which tries to address this issue. It uses memories as computing units, hence reducing the data transfers between memory and processing cores. However, the application of present PIM techniques is restricted by their limited functionality and inability to process large amounts of data efficiently. In this thesis, we propose novel techniques which exploit the analog properties of emerging memory technologies. Not only do these support more complex functions such as addition, multiplication, and search but also manage and process large data more efficiently. We present a new blocked PIM architecture which uses inter-block interconnects to accelerate data intensive processing. We also introduce a heterogeneous architecture having general purpose cores and PIM-enable memory and a data-dependent task allocation scheme for it. We also apply application specific optimizations and approximation techniques to further design accelerators for neural networks and database query systems. While we design a multiplication-by-constant hardware for neural networks, query processing is accelerated by a novel in-memory nearest search technique. Our neural network accelerator achieves 113.9x higher energy efficiency and 56.3x speedup as compared to AMD GPU. Also, the query accelerator provides 49.3x performance speedup and 32.9x energy savings as compared to recent Intel CPU.