March AB, March AB1: new March tests for unlinked dynamic memory faults

Among the different types of algorithms proposed to test static random access memories (SRAMs), March tests have proven to be faster, simpler and regularly structured. New memory production technologies introduce new classes of faults usually referred to as dynamic memory faults. A few March tests for dynamic fault, with different fault coverage, have been published. In this paper, we propose new March tests targeting unlinked dynamic faults with lower complexity than published ones. Comparison results show that the proposed March tests provide the same fault coverage of the known ones, but they reduce the test complexity, and therefore the test tim

A Low-Cost FPGA-Based Test and Diagnosis Architecture for SRAMs

The continues improvement of manufacturing technologies allows the realization of integrated circuits containing an ever increasing number of transistors. A major part of these devices is devoted to realize SRAM blocks. Test and diagnosis of SRAM circuits are therefore an important challenge for improving quality of next generation integrated circuits. This paper proposes a flexible platform for testing and diagnosis of SRAM circuits. The architecture is based on the use of a low cost FPGA based board allowing high diagnosability while keeping costs at a very low leve

Automatic March tests generation for multi-port SRAMs

Testing of Multi-Port (MP) SRAMs requires special tests since the multiple and simultaneous access can sensitize faults that are different from the conventional single-port memory faults. In spite of their growing use, few works have been published on testing MP memories. In addition, most of the published work concentrated only on two ports memories (i.e., 2P memories). This paper presents a methodology to automatically generate march tests for MP memories. It is based on generations of single port memory march test firstly, then extending it to test a generic MP SRAMs. A set of experimental results shows the effectiveness of the proposed solutio

March CRF: an Efficient Test for Complex Read Faults in SRAM Memories

In this paper we study Complex Read Faults in SRAMs, a combination of various malfunctions that affect the read operation in nanoscale memories. All the memory elements involved in the read operation are studied, underlining the causes of the realistic faults concerning this operation. The requirements to cover these fault models are given. We show that the different causes of read failure are independent and may coexist in nanoscale SRAMs, summing their effects and provoking Complex Read Faults, CRFs. We show that the test methodology to cover this new read faults consists in test patterns that match the requirements to cover all the different simple read fault models. We propose a low complexity (?2N) test, March CRF, that covers effectively all the realistic Complex Read Fault

XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference

Binary Neural Networks (BNNs) are promising to deliver accuracy comparable to conventional deep neural networks at a fraction of the cost in terms of memory and energy. In this paper, we introduce the XNOR Neural Engine (XNE), a fully digital configurable hardware accelerator IP for BNNs, integrated within a microcontroller unit (MCU) equipped with an autonomous I/O subsystem and hybrid SRAM / standard cell memory. The XNE is able to fully compute convolutional and dense layers in autonomy or in cooperation with the core in the MCU to realize more complex behaviors. We show post-synthesis results in 65nm and 22nm technology for the XNE IP and post-layout results in 22nm for the full MCU indicating that this system can drop the energy cost per binary operation to 21.6fJ per operation at 0.4V, and at the same time is flexible and performant enough to execute state-of-the-art BNN topologies such as ResNet-34 in less than 2.2mJ per frame at 8.9 fps.Comment: 11 pages, 8 figures, 2 tables, 3 listings. Accepted for presentation at CODES'18 and for publication in IEEE Transactions on Computer-Aided Design of Circuits and Systems (TCAD) as part of the ESWEEK-TCAD special issu

A Unique March Test Algorithm for the Wide Spread of Realistic Memory Faults in SRAMs

Among the different types of algorithms proposed to test static random access memories (SRAMs), march tests have proven to be faster, simpler and regularly structured. A large number of march tests with different fault coverage have been published. Usually different march tests detect only a specific set of memory faults. The always growing memory production technology introduces new classes of fault, making a key hurdle the generation of new march tests. The aim of this paper is to target the whole set of realistic fault model and to provide a unique march test able to reduce the test complexity of 15.4% than state-of-the-art march algorith

Multi-port Memory Design for Advanced Computer Architectures

In this thesis, we describe and evaluate novel memory designs for multi-port on-chip and off-chip use in advanced computer architectures. We focus on combining multi-porting and evaluating the performance over a range of design parameters. Multi-porting is essential for caches and shared-data systems, especially multi-core System-on-chips (SOC). It can significantly increase the memory access throughput. We evaluate FinFET voltage-mode multi-port SRAM cells using different metrics including leakage current, static noise margin and read/write performance. Simulation results show that single-ended multi-port FinFET SRAMs with isolated read ports offer improved read stability and flexibility over classical double-ended structures at the expense of write performance. By increasing the size of the access transistors, we show that the single-ended multi-port structures can achieve equivalent write performance to the classical double-ended multi-port structure for 9% area overhead. Moreover, compared with CMOS SRAM, FinFET SRAM has better stability and standby power. We also describe new methods for the design of FinFET current-mode multi-port SRAM cells. Current-mode SRAMs avoid the full-swing of the bitline, reducing dynamic power and access time. However, that comes at the cost of voltage drop, which compromises stability. The design proposed in this thesis utilizes the feature of Independent Gate (IG) mode FinFET, which can leverage threshold voltage by controlling the back gate voltage, to merge two transistors into one through high-Vt and low-Vt transistors. This design not only reduces the voltage drop, but it also reduces the area in multi-port current-mode SRAM design. For off-chip memory, we propose a novel two-port 1-read, 1-write (1R1W) phasechange memory (PCM) cell, which significantly reduces the probability of blocking at the bank levels. Different from the traditional PCM cell, the access transistors are at the top and connected to the bitline. We use Verilog-A to model the behavior of Ge2Sb2Te5 (GST: the storage component). We evaluate the performance of the two-port cell by transistor sizing and voltage pumping. Simulation results show that pMOS transistor is more practical than nMOS transistor as the access device when both area and power are considered. The estimated area overhead is 1.7�, compared to single-port PCM cell. In brief, the contribution we make in this thesis is that we propose and evaluate three different kinds of multi-port memories that are favorable for advanced computer architectures

X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories

Silicon-based Static Random Access Memories (SRAM) and digital Boolean logic have been the workhorse of the state-of-art computing platforms. Despite tremendous strides in scaling the ubiquitous metal-oxide-semiconductor transistor, the underlying \textit{von-Neumann} computing architecture has remained unchanged. The limited throughput and energy-efficiency of the state-of-art computing systems, to a large extent, results from the well-known \textit{von-Neumann bottleneck}. The energy and throughput inefficiency of the von-Neumann machines have been accentuated in recent times due to the present emphasis on data-intensive applications like artificial intelligence, machine learning \textit{etc}. A possible approach towards mitigating the overhead associated with the von-Neumann bottleneck is to enable \textit{in-memory} Boolean computations. In this manuscript, we present an augmented version of the conventional SRAM bit-cells, called \textit{the X-SRAM}, with the ability to perform in-memory, vector Boolean computations, in addition to the usual memory storage operations. We propose at least six different schemes for enabling in-memory vector computations including NAND, NOR, IMP (implication), XOR logic gates with respect to different bit-cell topologies $-$ the 8T cell and the 8$^+$T Differential cell. In addition, we also present a novel \textit{`read-compute-store'} scheme, wherein the computed Boolean function can be directly stored in the memory without the need of latching the data and carrying out a subsequent write operation. The feasibility of the proposed schemes has been verified using predictive transistor models and Monte-Carlo variation analysis.Comment: This article has been accepted in a future issue of IEEE Transactions on Circuits and Systems-I: Regular Paper