The recently established technologies in the areas of distributed measurement and intelligent
information processing systems, e.g., Cyber Physical Systems (CPS), Ambient
Intelligence/Ambient Assisted Living systems (AmI/AAL), the Internet of Things
(IoT), and Industry 4.0 have increased the demand for the development of intelligent
integrated multi-sensory systems as to serve rapid growing markets [1, 2]. These increase
the significance of complex measurement systems, that incorporate numerous advanced
methodological implementations including electronics circuit, signal processing,
and multi-sensory information fusion. In particular, in multi-sensory cognition applications,
to design such systems, the skill-required tasks, e.g., method selection, parameterization,
model analysis, and processing chain construction are elaborated with immense
effort, which conventionally are done manually by the expert designer. Moreover, the
strong technological competition imposes even more complicated design problems with
multiple constraints, e.g., cost, speed, power consumption,
exibility, and reliability.
Thus, the conventional human expert based design approach may not be able to cope
with the increasing demand in numbers, complexity, and diversity. To alleviate the issue,
the design automation approach has been the topic for numerous research works [3-14]
and has been commercialized to several products [15-18]. Additionally, the dynamic
adaptation of intelligent multi-sensor systems is the potential solution for developing
dependable and robust systems. Intrinsic evolution approach and self-x properties ,
which include self-monitoring, -calibrating/trimming, and -healing/repairing, are among
the best candidates for the issue. Motivated from the ongoing research trends and based
on the background of our research work [12, 13] among the pioneers in this topic, the
research work of the thesis contributes to the design automation of intelligent integrated
In this research work, the Design Automation for Intelligent COgnitive system with self-
X properties, the DAICOX, architecture is presented with the aim of tackling the design
effort and to providing high quality and robust solutions for multi-sensor intelligent
systems. Therefore, the DAICOX architecture is conceived with the defined goals as
Perform front to back complete processing chain design with automated method
selection and parameterization,
Provide a rich choice of pattern recognition methods to the design method pool,
Associate design information via interactive user interface and visualization along
with intuitive visual programming,
Deliver high quality solutions outperforming conventional approaches by using
Gain the adaptability, reliability and robustness of designed solutions with self-x
Derived from the goals, several scientific methodological developments and implementations,
particularly in the areas of pattern recognition and computational intelligence,
will be pursued as part of the DAICOX architecture in the research work of this thesis.
The method pool is aimed to contain a rich choice of methods and algorithms covering
data acquisition and sensor configuration, signal processing and feature computation,
dimensionality reduction, and classification. These methods will be selected and parameterized
automatically by the DAICOX design optimization to construct a multi-sensory
cognition processing chain. A collection of non-parametric feature quality assessment
functions for the purpose of Dimensionality Reduction (DR) process will be presented.
In addition, to standard DR methods, the variations of feature selection method, in
particular, feature weighting will be proposed. Three different classification categories
shall be incorporated in the method pool. Hierarchical classification approach will be
proposed and developed to serve as a multi-sensor fusion architecture at the decision
level. Beside multi-class classification, one-class classification methods, e.g., One-Class
SVM and NOVCLASS will be presented to extend functionality of the solutions, in particular,
anomaly and novelty detection. DAICOX is conceived to effectively handle the
problem of method selection and parameter setting for a particular application yielding
high performance solutions. The processing chain construction tasks will be carried
out by meta-heuristic optimization methods, e.g., Genetic Algorithms (GA) and Particle
Swarm Optimization (PSO), with multi-objective optimization approach and model
analysis for robust solutions. In addition, to the automated system design mechanisms,
DAICOX will facilitate the design tasks with intuitive visual programming and various
options of visualization. Design database concept of DAICOX is aimed to allow the
reusability and extensibility of the designed solutions gained from previous knowledge.
Thus, the cooperative design of machine and knowledge from the design expert can also
be utilized for obtaining fully enhanced solutions. In particular, the integration of self-x
properties as well as intrinsic optimization into the system is proposed to gain enduring
reliability and robustness. Hence, DAICOX will allow the inclusion of dynamically
reconfigurable hardware instances to the designed solutions in order to realize intrinsic
optimization and self-x properties.
As a result from the research work in this thesis, a comprehensive intelligent multisensor
system design architecture with automated method selection, parameterization,
and model analysis is developed with compliance to open-source multi-platform software.It is integrated with an intuitive design environment, which includes visual programming
concept and design information visualizations. Thus, the design effort is minimized as
investigated in three case studies of different application background, e.g., food analysis
(LoX), driving assistance (DeCaDrive), and magnetic localization. Moreover, DAICOX
achieved better quality of the solutions compared to the manual approach in all cases,
where the classification rate was increased by 5.4%, 0.06%, and 11.4% in the LoX,
DeCaDrive, and magnetic localization case, respectively. The design time was reduced
by 81.87% compared to the conventional approach by using DAICOX in the LoX case
study. At the current state of development, a number of novel contributions of the thesis
are outlined below.
Automated processing chain construction and parameterization for the design of
signal processing and feature computation.
Novel dimensionality reduction methods, e.g., GA and PSO based feature selection
and feature weighting with multi-objective feature quality assessment.
A modification of non-parametric compactness measure for feature space quality
Decision level sensor fusion architecture based on proposed hierarchical classification
approach using, i.e., H-SVM.
A collection of one-class classification methods and a novel variation, i.e.,
Automated design toolboxes supporting front to back design with automated
model selection and information visualization.
In this research work, due to the complexity of the task, neither all of the identified goals
have been comprehensively reached yet nor has the complete architecture definition been
fully implemented. Based on the currently implemented tools and frameworks, ongoing
development of DAICOX is pursuing towards the complete architecture. The potential
future improvements are the extension of method pool with a richer choice of methods
and algorithms, processing chain breeding via graph based evolution approach, incorporation
of intrinsic optimization, and the integration of self-x properties. According to
these features, DAICOX will improve its aptness in designing advanced systems to serve
the increasingly growing technologies of distributed intelligent measurement systems, in
particular, CPS and Industrie 4.0.
This tutorial describes how to accurately measure signal power using the FFT. The different effects that introduce errors during FFT processing are described and it is explained how they can be avoided or compensated.
In this paper, we show the feasibility of low supply voltage for SRAM (Static Random Access Memory) by adding error correction coding (ECC). In SRAM, the memory matrix needs to be powered for data retentive standby operation, resulting in standby leakage current. Particularly for low duty- cycle systems, the energy consumed due to standby leakage current can become significant. Lowering the supply voltage (VDD) during standby mode to below the specified data retention voltage (DRV) helps decrease the leakage current. At these VDD levels errors start to appear, which we can remedy by adding ECC. We show in this paper that addition of a simple single error correcting (SEC) ECC enables us to decrease the leakage current by 45% and leakage power by 72%. We verify this on a large set of commercially available standard 40nm SRAMs.
Emerging Memories (EMs) could benefit from Error Correcting Codes (ECCs) able to correct few errors in a few nanoseconds. The low latency is necessary to meet the DRAM- like and/or eXecuted-in-Place requirements of Storage Class Memory devices. The error correction capability would help manufacturers to cope with unknown failure mechanisms and to fulfill the market demand for a rapid increase in density. This paper shows the design of an ECC decoder for a shortened BCH code with 256-data-bit page able to correct three errors in less than 3 ns. The tight latency constraint is met by pre-computing the coefficients of carefully chosen Error Locator Polynomials, by optimizing the operations in the Galois Fields and by resorting to a fully parallel combinatorial implementation of the decoder. The latency and the area occupancy are first estimated by the number of elementary gates to traverse, and by the total number of elementary gates of the decoder. Eventually, the implementation of the solution by Synopsys topographical synthesis methodology in 54nm logic gate length CMOS technology gives a latency lower than 3 ns and a total area less than \(250 \cdot 10^3 \mu m^2\).
Magnetic spin-based memory technologies are a promising solution to overcome the incoming limits of microelectronics. Nevertheless, the long write latency and high write energy of these memory technologies compared to SRAM make it difficult to use these for fast microprocessor memories, such as L1- Caches. However, the recent advent of the Spin Orbit Torque (SOT) technology changed the story: indeed, it potentially offers a writing speed comparable to SRAM with a much better density as SRAM and an infinite endurance, paving the way to a new paradigm in processor architectures, with introduction of non- volatility in all the levels of the memory hierarchy towards full normally-off and instant-on processors. This paper presents a full design flow, from device to system, allowing to evaluate the potential of SOT for microprocessor cache memories and very encouraging simulation results using this framework.
The capacity of embedded memory on LSIs has kept increasing. It is important to reduce the leakage power of embedded memory for low-power LSIs. In fact, the ITRS predicts that the leakage power in embedded memory will account for 40% of all power consumption by 2024 . A spin transfer torque magneto-resistance random access memory (STT-MRAM) is promising for use as non-volatile memory to reduce the leakage power. It is useful because it can function at low voltages and has a lifetime of over 1016 write cycles . In addition, the STT-MRAM technology has a smaller bit cell than an SRAM. Making the STT-MRAM is suitable for use in high-density products [3–7]. The STT-MRAM uses magnetic tunnel junction (MTJ). The MTJ has two states: a parallel state and an anti-parallel state. These states mean that the magnetization direction of the MTJ’s layers are the same or different. The directions pair determines the MTJ’s magneto- resistance value. The states of MTJ can be changed by the current flowing. The MTJ resistance becomes low in the parallel state and high in the anti-parallel state. The MTJ potentially operates at less than 0.4 V . In other hands, it is difficult to design peripheral circuitry for an STT-MRAM array at such a low voltage. In this paper, we propose a counter-based read circuit that functions at 0.4 V, which is tolerant of process variation and temperature fluctuation.
3D integration of solid-state memories and logic, as demonstrated by the Hybrid Memory Cube (HMC), offers major opportunities for revisiting near-memory computation and gives new hope to mitigate the power and performance losses caused by the “memory wall”. In this paper we present the first exploration steps towards design of the Smart Memory Cube (SMC), a new Processor-in-Memory (PIM) architecture that enhances the capabilities of the logic-base (LoB) in HMC. An accurate simulation environment has been developed, along with a full featured software stack. All offloading and dynamic overheads caused by the operating system, cache coherence, and memory management are considered, as well. Benchmarking results demonstrate up to 2X performance improvement in comparison with the host SoC, and around 1.5X against a similar host-side accelerator. Moreover, by scaling down the voltage and frequency of PIM’s processor it is possible to reduce energy by around 70% and 55% in comparison with the host and the accelerator, respectively.
Multiple-channel die-stacked DRAMs have been used for maximizing the performance and minimizing the power of memory access in 2.5D/3D system chips. Stacked DRAM dies can be used as a cache for the processor die in 2.5D/3D system chips. Typically, modern processor system-on-chips (SOCs) have three-level caches, L1, L2, and L3. Could the DRAM cache be used to replace which level of caches? In this paper, we derive an inequality which can aid the designer to check if the designed DRAM cache can provide better performance than the L3 cache. Also, design considerations of DRAM caches for meet the inequality are discussed. We find that a dilemma of the DRAM cache access time and associativity exists for providing better performance than the L3 cache. Organizing multiple channels into a DRAM cache is proposed to cope with the dilemma.
This paper briefly discusses a new architecture, Computation-In-Memory (CIM Architecture), which performs “processing-in-memory”. It is based on the integration of storage and computation in the same physical location (crossbar topology) and the use of non-volatile resistive-switching technology (memristive devices or memristors in short) instead of CMOS technology. The architecture has the potential of improving the energy-delay product, computing efficiency and performance area by at least two orders of magnitude.
Three-dimensional (3D) integration using through- silicon via (TSV) has been used for memory designs. Content addressable memory (CAM) is an important component in digital systems. In this paper, we propose an evaluation tool for 3D CAMs, which can aid the designer to explore the delay and power of various partitioning strategies. Delay, power, and energy models of 3D CAM with respect to different architectures are built as well.