Computation-in-Memory for Modern Applications using Emerging Technologies

Modern applications like Genomics and Machine Learning (ML) hold the potential to reshape our understanding of diseases’ genetic origins and guide machines in executing tasks and making predictions without our explicit programming. The successful, widespread integration of these modern applications can usher in advancements in di-agnostics,...

SieveMem: A Computation-in-Memory Architecture for Fast and Accurate Pre-Alignment

The high execution time of DNA sequence alignment negatively affects many genomic studies that rely on sequence alignment results. Pre-alignment filtering was introduced as a step before alignment to reduce the execution time of short-read sequence alignment greatly. With its success, i.e., achieving high accuracy and thus removing...

SparseMEM: Energy-efficient Design for In-memory Sparse-based Graph Processing

Performing analysis on large graph datasets in an energy-efficient manner has posed a significant challenge; not only due to excessive data movements and poor locality, but also due to the non-optimal use of high sparsity of such datasets. The latter leads to a waste of resources as the computation is also performed on zero's operands which do...

Lightspeed Binary Neural Networks using Optical Phase-Change Materials

This paper investigates the potential of a compute-in-memory core based on optical Phase Change Materials (oPCMs) to speed up and reduce the energy consumption of the Matrix-Matrix-Multiplication operation. The paper also proposes a new data mapping for Binary Neural Networks (BNNs) tailored for our oPCM core. The preliminary results show a...

Efficient Signed Arithmetic Multiplication on Memristor-based Crossbar

The vast potential of memristor-based computation-in-memory (CIM) engines has mainly triggered the mapping of best-suited applications. Nevertheless, with additional support, existing applications can also benefit from CIM. In particular, this paper proposes an energy and area-efficient CIM-based methodology to perform arithmetic signed...

KrakenOnMem: A Memristor-Augmented HW/SW Framework for Taxonomic Profiling

State-of-the-art taxonomic profilers that comprise the first step in larger-context metagenomic studies have proven to be computationally intensive, i.e., while accurate, they come at the cost of high latency and energy consumption. Table Lookup operation is a primary bottleneck of today's profilers. In this paper, we first propose TL-PIM, a...

Demeter: A Fast and Energy-Efficient Food Profiler Using Hyperdimensional Computing in Memory

Food profiling is an essential step in any food monitoring system needed to prevent health risks and potential frauds in the food industry. Significant improvements in sequencing technologies are pushing food profiling to become the main computational bottleneck. State-of-the-art profilers are unfortunately too costly for food profiling. Our...

CIM-based Robust Logic Accelerator using 28 nm STT-MRAM Characterization Chip Tape-out

Spin-transfer torque magnetic random access memory (STT-MRAM) based computation-in-memory (CIM) architectures have shown great prospects for an energy-efficient computing. However, device variations and non-idealities narrow down the sensing margin that severely impacts the computing accuracy. In this work, we propose an adaptive referencing...