Accurate and Energy-Efficient Bit-Slicing for RRAM-Based Neural Networks

Computation-in-memory (CIM) paradigm leverages emerging memory technologies such as resistive random access memories (RRAMs) to process the data within the memory itself. This alleviates the memory-processor bottleneck resulting in much higher hardware efficiency compared to von-Neumann architecture-based conventional hardware. Hence, CIM...

A 115.1 TOPS/W, 12.1 TOPS/mm²Computation-in-Memory using Ring-Oscillator based ADC for Edge AI

Analog computation-in-memory (CIM) architecture alleviates massive data movement between the memory and the processor, thus promising great prospects to accelerate certain computational tasks in an energy-efficient manner. However, data converters involved in these architectures typically achieve the required computing accuracy at the expense...

Energy-efficient In-Memory Address Calculation

Computation-in-Memory (CIM) is an emerging computing paradigm to address memory bottleneck challenges in computer architecture. A CIM unit cannot fully replace a general-purpose processor. Still, it significantly reduces the amount of data transfer between a traditional memory unit and the processor by enriching the transferred information....

System Design for Computation-in-Memory: From Primitive to Complex Functions

In recent years, we are witnessing a trend moving away from conventional computer architectures towards Computation-In-Memory (CIM) based on emerging memristor devices. This is due to the fact that the performance and energy efficiency of traditional computer architectures can no longer be increased at the same pace as before. The main barriers...

Accelerating RRAM Testing with a Low-cost Computation-in-Memory based DFT

Emerging non-volatile resistive RAM (RRAM) device technology has shown great potential to cultivate not only high-density memory storage, but also energy-efficient computing units. However, the unique challenges related to RRAM fabrication process render the traditional memory testing solutions inefficient and inadequate for high product quality...

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...

Dealing with Non-Idealities in Memristor Based Computation-In-Memory Designs

Computation-In-Memory (CIM) using memristor devices provides an energy-efficient hardware implementation of arithmetic and logic operations for numerous applications, such as neuromorphic computing and database query. However, memristor-based CIM suffers from various non-idealities such as conductance drift, read disturb, wire parasitics,...

Referencing-in-Array Scheme for RRAM-based CIM Architecture

Resistive random access memory (RRAM) based computation-in-memory (CIM) architectures are attracting a lot of attention due to their potential in performing fast and energy-efficient computing. However, the RRAM variability and non-idealities limit the computing accuracy of such architectures, especially for multi-operand logic operations....