Fault Secure Encoder and Decoder for NanoMemory Applications

Memory cells have been protected from soft errors for more than a decade; due to the increase in soft error rate in logic circuits, the encoder and decoder circuitry around the memory blocks have become susceptible to soft errors as well and must also be protected. We introduce a new approach to design fault-secure encoder and decoder circuitry for memory designs. The key novel contribution of this paper is identifying and defining a new class of error-correcting codes whose redundancy makes the design of fault-secure detectors (FSD) particularly simple. We further quantify the importance of protecting encoder and decoder circuitry against transient errors, illustrating a scenario where the system failure rate (FIT) is dominated by the failure rate of the encoder and decoder. We prove that Euclidean geometry low-density parity-check (EG-LDPC) codes have the fault-secure detector capability. Using some of the smaller EG-LDPC codes, we can tolerate bit or nanowire defect rates of 10% and fault rates of 10^(-18) upsets/device/cycle, achieving a FIT rate at or below one for the entire memory system and a memory density of 10^(11) bit/cm^2 with nanowire pitch of 10 nm for memory blocks of 10 Mb or larger. Larger EG-LDPC codes can achieve even higher reliability and lower area overhead

Fault-tolerance techniques for hybrid CMOS/nanoarchitecture

The authors propose two fault-tolerance techniques for hybrid CMOS/nanoarchitecture implementing logic functions as look-up tables. The authors compare the efficiency of the proposed techniques with recently reported methods that use single coding schemes in tolerating high fault rates in nanoscale fabrics. Both proposed techniques are based on error correcting codes to tackle different fault rates. In the first technique, the authors implement a combined two-dimensional coding scheme using Hamming and Bose-Chaudhuri-Hocquenghem (BCH) codes to address fault rates greater than 5. In the second technique, Hamming coding is complemented with bad line exclusion technique to tolerate fault rates higher than the first proposed technique (up to 20). The authors have also estimated the improvement that can be achieved in the circuit reliability in the presence of Don-t Care Conditions. The area, latency and energy costs of the proposed techniques were also estimated in the CMOS domain

Combined Time and Information Redundancy for SEU-Tolerance in Energy-Efficient Real-Time Systems

Recently the trade-off between energy consumption and fault-tolerance in real-time systems has been highlighted. These works have focused on dynamic voltage scaling (DVS) to reduce dynamic energy dissipation and on time redundancy to achieve transient-fault tolerance. While the time redundancy technique exploits the available slack time to increase the fault-tolerance by performing recovery executions, DVS exploits slack time to save energy. Therefore we believe there is a resource conflict between the time-redundancy technique and DVS. The first aim of this paper is to propose the usage of information redundancy to solve this problem. We demonstrate through analytical and experimental studies that it is possible to achieve both higher transient fault-tolerance (tolerance to single event upsets (SEU)) and less energy using a combination of information and time redundancy when compared with using time redundancy alone. The second aim of this paper is to analyze the interplay of transient-fault tolerance (SEU-tolerance) and adaptive body biasing (ABB) used to reduce static leakage energy, which has not been addressed in previous studies. We show that the same technique (i.e. the combination of time and information redundancy) is applicable to ABB-enabled systems and provides more advantages than time redundancy alone

Automated Synthesis of SEU Tolerant Architectures from OO Descriptions

SEU faults are a well-known problem in aerospace environment but recently their relevance grew up also at ground level in commodity applications coupled, in this frame, with strong economic constraints in terms of costs reduction. On the other hand, latest hardware description languages and synthesis tools allow reducing the boundary between software and hardware domains making the high-level descriptions of hardware components very similar to software programs. Moving from these considerations, the present paper analyses the possibility of reusing Software Implemented Hardware Fault Tolerance (SIHFT) techniques, typically exploited in micro-processor based systems, to design SEU tolerant architectures. The main characteristics of SIHFT techniques have been examined as well as how they have to be modified to be compatible with the synthesis flow. A complete environment is provided to automate the design instrumentation using the proposed techniques, and to perform fault injection experiments both at behavioural and gate level. Preliminary results presented in this paper show the effectiveness of the approach in terms of reliability improvement and reduced design effort

Fault-tolerant computer study

A set of building block circuits is described which can be used with commercially available microprocessors and memories to implement fault tolerant distributed computer systems. Each building block circuit is intended for VLSI implementation as a single chip. Several building blocks and associated processor and memory chips form a self checking computer module with self contained input output and interfaces to redundant communications buses. Fault tolerance is achieved by connecting self checking computer modules into a redundant network in which backup buses and computer modules are provided to circumvent failures. The requirements and design methodology which led to the definition of the building block circuits are discussed

Programmable neural logic

Circuits of threshold elements (Boolean input, Boolean output neurons) have been shown to be surprisingly powerful. Useful functions such as XOR, ADD and MULTIPLY can be implemented by such circuits more efficiently than by traditional AND/OR circuits. In view of that, we have designed and built a programmable threshold element. The weights are stored on polysilicon floating gates, providing long-term retention without refresh. The weight value is increased using tunneling and decreased via hot electron injection. A weight is stored on a single transistor allowing the development of dense arrays of threshold elements. A 16-input programmable neuron was fabricated in the standard 2 μm double-poly, analog process available from MOSIS. We also designed and fabricated the multiple threshold element introduced in [5]. It presents the advantage of reducing the area of the layout from O(n^2) to O(n); (n being the number of variables) for a broad class of Boolean functions, in particular symmetric Boolean functions such as PARITY. A long term goal of this research is to incorporate programmable single/multiple threshold elements, as building blocks in field programmable gate arrays

VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing

The hardware implementation of deep neural networks (DNNs) has recently received tremendous attention: many applications in fact require high-speed operations that suit a hardware implementation. However, numerous elements and complex interconnections are usually required, leading to a large area occupation and copious power consumption. Stochastic computing has shown promising results for low-power area-efficient hardware implementations, even though existing stochastic algorithms require long streams that cause long latencies. In this paper, we propose an integer form of stochastic computation and introduce some elementary circuits. We then propose an efficient implementation of a DNN based on integral stochastic computing. The proposed architecture has been implemented on a Virtex7 FPGA, resulting in 45% and 62% average reductions in area and latency compared to the best reported architecture in literature. We also synthesize the circuits in a 65 nm CMOS technology and we show that the proposed integral stochastic architecture results in up to 21% reduction in energy consumption compared to the binary radix implementation at the same misclassification rate. Due to fault-tolerant nature of stochastic architectures, we also consider a quasi-synchronous implementation which yields 33% reduction in energy consumption w.r.t. the binary radix implementation without any compromise on performance.Comment: 11 pages, 12 figure

System-on-chip Computing and Interconnection Architectures for Telecommunications and Signal Processing

This dissertation proposes novel architectures and design techniques targeting SoC building blocks for telecommunications and signal processing applications. Hardware implementation of Low-Density Parity-Check decoders is approached at both the algorithmic and the architecture level. Low-Density Parity-Check codes are a promising coding scheme for future communication standards due to their outstanding error correction performance. This work proposes a methodology for analyzing effects of finite precision arithmetic on error correction performance and hardware complexity. The methodology is throughout employed for co-designing the decoder. First, a low-complexity check node based on the P-output decoding principle is designed and characterized on a CMOS standard-cells library. Results demonstrate implementation loss below 0.2 dB down to BER of 10^{-8} and a saving in complexity up to 59% with respect to other works in recent literature. High-throughput and low-latency issues are addressed with modified single-phase decoding schedules. A new "memory-aware" schedule is proposed requiring down to 20% of memory with respect to the traditional two-phase flooding decoding. Additionally, throughput is doubled and logic complexity reduced of 12%. These advantages are traded-off with error correction performance, thus making the solution attractive only for long codes, as those adopted in the DVB-S2 standard. The "layered decoding" principle is extended to those codes not specifically conceived for this technique. Proposed architectures exhibit complexity savings in the order of 40% for both area and power consumption figures, while implementation loss is smaller than 0.05 dB. Most modern communication standards employ Orthogonal Frequency Division Multiplexing as part of their physical layer. The core of OFDM is the Fast Fourier Transform and its inverse in charge of symbols (de)modulation. Requirements on throughput and energy efficiency call for FFT hardware implementation, while ubiquity of FFT suggests the design of parametric, re-configurable and re-usable IP hardware macrocells. In this context, this thesis describes an FFT/IFFT core compiler particularly suited for implementation of OFDM communication systems. The tool employs an accuracy-driven configuration engine which automatically profiles the internal arithmetic and generates a core with minimum operands bit-width and thus minimum circuit complexity. The engine performs a closed-loop optimization over three different internal arithmetic models (fixed-point, block floating-point and convergent block floating-point) using the numerical accuracy budget given by the user as a reference point. The flexibility and re-usability of the proposed macrocell are illustrated through several case studies which encompass all current state-of-the-art OFDM communications standards (WLAN, WMAN, xDSL, DVB-T/H, DAB and UWB). Implementations results are presented for two deep sub-micron standard-cells libraries (65 and 90 nm) and commercially available FPGA devices. Compared with other FFT core compilers, the proposed environment produces macrocells with lower circuit complexity and same system level performance (throughput, transform size and numerical accuracy). The final part of this dissertation focuses on the Network-on-Chip design paradigm whose goal is building scalable communication infrastructures connecting hundreds of core. A low-complexity link architecture for mesochronous on-chip communication is discussed. The link enables skew constraint looseness in the clock tree synthesis, frequency speed-up, power consumption reduction and faster back-end turnarounds. The proposed architecture reaches a maximum clock frequency of 1 GHz on 65 nm low-leakage CMOS standard-cells library. In a complex test case with a full-blown NoC infrastructure, the link overhead is only 3% of chip area and 0.5% of leakage power consumption. Finally, a new methodology, named metacoding, is proposed. Metacoding generates correct-by-construction technology independent RTL codebases for NoC building blocks. The RTL coding phase is abstracted and modeled with an Object Oriented framework, integrated within a commercial tool for IP packaging (Synopsys CoreTools suite). Compared with traditional coding styles based on pre-processor directives, metacoding produces 65% smaller codebases and reduces the configurations to verify up to three orders of magnitude