Endurance Management for Resistive Logic-In-Memory Computing Architectures

Resistive Random Access Memory (RRAM) is a promising non-volatile memory technology which enables modern in-memory computing architectures. Although RRAMs are known to be superior to conventional memories in many aspects, they suffer from a low write endurance. In this paper, we focus on balancing memory write traffic as a solution to extend the lifetime of resistive crossbar architectures. As a case study, we monitor the write traffic in a Programmable Logic-in-Memory (PLiM) architecture, and propose an endurance management scheme for it. The proposed endurance-aware compilation is capable of handling different trade-offs between write balance, latency, and area of the resulting PLiM implementations. Experimental evaluations on a set of benchmarks including large arithmetic and control functions show that the standard deviation of writes can be reduced by 86.65% on average compared to a naive compiler, while the average number of instructions and RRAM devices also decreases by 36.45% and 13.67%, respectively

Fault Injection in Native Logic-in-Memory Computation on Neuromorphic Hardware

Logic-in-memory (LIM) describes the execution of logic gates within memristive crossbar structures, promising to improve performance and energy efficiency. Utilizing only binary values, LIM particularly excels in accelerating binary neural networks, shifting it in the focus of edge applications. Considering its potential, the impact of faults on BNNs accelerated with LIM still lacks investigation. In this paper, we propose faulty logic-in-memory (FLIM), a fault injection platform capable of executing full-fledged BNNs on LIM while injecting in-field faults. The results show that FLIM runs a single MNIST picture 66754x faster than the state of the art by offering a fine-grained fault injection methodology

Inversion optimization in majority-inverter graphs

Many emerging nanotechnologies realize majority gates as primitive building blocks and they benefit from a majority-based synthesis. Recently, Majority-Inverter Graphs (MIGs) have been introduced to abstract these new technologies. We present optimization techniques for MIGs that aim at rewriting the complemented edges of the graph without changing its shape. We demonstrate the performance of our optimization techniques by considering three cases of emerging technology design: semi-custom digital design using Spin Wave Devices (SWDs) and Quantum-Dot Cellular Automata (QCA); and logic in-memory operation within Resistive Random Access Memories (RRAMs). Our experimental results show that SWD and QCA technologies benefit from complemented edges minimization. Area, delay, and power of SWD-based circuits are improved by 13.8%, 21.1%, and 9.2% respectively, while the number of QCA cells in QCA-based circuits can be decreased by 4.9% on average. Reductions of 14.4% and 12.4% in the number of devices and sequential steps respectively can be achieved for RRAMs when the number of nodes with exactly one complemented input is increased during MIG optimization

Inversion Minimization in Majority-Inverter Graphs

In this paper, we present new optimization techniques for the recently introduced Majority-Inverter Graph (MIG). Our optimizations exploit intrinsic algebraic properties of MIGs and aim at rewriting the complemented edges of the graph without changing its shape. Two exact algorithms are proposed to minimize the number of complemented edges in the graph. The former is a dynamic programming method for trees; the latter finds the exact solution with a minimum number of inversions using Boolean satisfiability (SAT). We also describe a heuristic rule based algorithm to minimize complemented edges using local transformations. Experimental results for the exact algorithm to fanout-free regions show an average reduction of 12.8% on the EPFL benchmark suite. Applying the heuristic method on the same instances leads to a total improvement of 60.2%

New Logic-In-Memory Paradigms: An Architectural and Technological Perspective

Processing systems are in continuous evolution thanks to the constant technological advancement and architectural progress. Over the years, computing systems have become more and more powerful, providing support for applications, such as Machine Learning, that require high computational power. However, the growing complexity of modern computing units and applications has had a strong impact on power consumption. In addition, the memory plays a key role on the overall power consumption of the system, especially when considering data-intensive applications. These applications, in fact, require a lot of data movement between the memory and the computing unit. The consequence is twofold: Memory accesses are expensive in terms of energy and a lot of time is wasted in accessing the memory, rather than processing, because of the performance gap that exists between memories and processing units. This gap is known as the memory wall or the von Neumann bottleneck and is due to the different rate of progress between complementary metal-oxide semiconductor (CMOS) technology and memories. However, CMOS scaling is also reaching a limit where it would not be possible to make further progress. This work addresses all these problems from an architectural and technological point of view by: (1) Proposing a novel Configurable Logic-in-Memory Architecture that exploits the in-memory computing paradigm to reduce the memory wall problem while also providing high performance thanks to its flexibility and parallelism; (2) exploring a non-CMOS technology as possible candidate technology for the Logic-in-Memory paradigm