The conventional digital hardware computational blocks with different structures are designed to compute the precise results of the assigned calculations. The main contribution of our proposed Bio-inspired Imprecise Computational blocks (BICs) is that they are designed to provide an applicable estimation of the result instead of its precise value at a lower cost. These novel structures are more efficient in terms of area, speed, and power consumption with respect to their precise rivals. Complete descriptions of sample BIC adder and multiplier structures as well as their error behaviors and synthesis results are introduced in this paper. It is then shown that these BIC structures can be exploited to efficiently implement a three-layer face recognition neural network and the hardware defuzzification block of a fuzzy processor.

Bio-Inspired Imprecise Computational Blocks for Efficient VLSI Implementation of Soft-Computing Applications

This report describes the progress made towards the completion of a specific task on error-correcting coding. The proposed research consisted of investigating the use of modulation block codes as the inner code of a concatenated coding system in order to improve the overall space link communications performance. The study proposed to identify and analyze candidate codes that will complement the performance of the overall coding system which uses the interleaved RS (255,223) code as the outer code.

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Error-correction coding

A multi-functional in-memory inference processor integrated circuit (IC) in a 65-nm CMOS process is presented. The prototype employs a deep in-memory architecture (DIMA), which enhances both energy efficiency and throughput over conventional digital architectures via simultaneous access of multiple rows of a standard 6T bitcell array (BCA) per precharge, and embedding column pitch-matched low-swing analog processing at the BCA periphery. In doing so, DIMA exploits the synergy between the dataflow of machine learning (ML) algorithms and the SRAM architecture to reduce the dominant energy cost due to data movement. The prototype IC incorporates a 16-kB SRAM array and supports four commonly used ML algorithms—the support vector machine, template matching, $k$ -nearest neighbor, and the matched filter. Silicon measured results demonstrate simultaneous gains (dot product mode) in energy efficiency of 10 $\times $ and in throughput of 5.3 $\times $ leading to a 53 $\times $ reduction in the energy-delay product with negligible ( $\le $ 1%) degradation in the decision-making accuracy, compared with the conventional 8-b fixed-point single-function digital implementations.

A Multi-Functional In-Memory Inference Processor Using a Standard 6T SRAM Array

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Logical Effort Designing Fast Cmos Circuits

SRAM suffers read-disturb and write failures at a low supply voltage, especially at deep subthreshold operation. This study proposes a 9T-SRAM cell with a data-aware-feedback-cutoff (DAFC) scheme to enlarge the write margin and dynamic-read-decoupled (DRD) scheme to prevent read-disturb for achieving deep subthreshold operation. A 30 mV negative-pumped wordline scheme is employed to suppress bitline leakage current. The fabricated 90 nm 32 Kb 9T-SRAM macro achieves 130 mV VDDmin. All the 32 Kb 9T cells are stable across read and write operations when operated at 105 mV.

A 130 mV SRAM With Expanded Write and Read Margins for Subthreshold Applications

In this paper, a voltage-scaled SRAM for both error-free and error-tolerant applications is presented that dynamically manages the energy/quality trade-off based on application need. Two variation-resilient techniques, write assist and Error Correcting Code, are selectively applied to bit positions having larger impact on the overall quality, while jointly performing voltage scaling to improve overall energy efficiency. The impact of process variations, voltage and temperature on the energy-quality tradeoff is investigated. A 28 nm CMOS 32 kb SRAM shows 35% energy savings at iso-quality and operates at a supply 220 mV below a baseline voltage-scaled SRAM, at the cost of 1.5% area penalty. The impact of the SRAM quality at the system level is evaluated by adopting a H.264 video decoder as case study.

SRAM for Error-Tolerant Applications With Dynamic Energy-Quality Management in 28 nm CMOS

The demand of ultralow-power circuits has significantly increased in the last few years. Owing to its great potential in energy savings, the use of supply voltage near or below the transistors' threshold voltages has gained particular attention. Designing these kinds of circuits is still a challenge, particularly when latest advanced process technologies are employed. This brief proposes novel analytical delay models for CMOS circuits running in the subthreshold regime. The delay models proposed here take the effects of the process variability and of the transient variation of the transistors' on-current during the switching into account. Owing to this, delays are predicted with accuracy significantly higher than existing accurate delay models. Furthermore, the novel models are also suitable for gates with transistors' stacks.

Analytical Delay Model Considering Variability Effects in Subthreshold Domain

In this paper, approximate SRAMs are explored in the context of error-tolerant applications, in which energy is saved at the cost of the occurrence of read/write errors (i.e., signal quality degradation). This analysis investigates variation-resilient techniques that enable dynamic management of the energy-quality tradeoff down to the bit level. In these techniques, the different impacts of errors on quality at different bit positions are explicitly considered as key enabler of energy savings that are far larger than a simple voltage scaling. The analysis is based on the experimental results in an energy-quality scalable 28-nm SRAM and the extrapolation to a wide range of conditions through the models that combine the individual energy contributions. Results show that the joint adoption of multiple bit-level techniques provides substantially larger energy gains than individual techniques. Compared with the simple voltage scaling at isoquality, the joint adoption of these techniques can provide more than $2\times $ energy reduction at negligible area penalty. Energy savings turn out to be highly sensitive to the choice of joint techniques, thus showing the crucial importance of dynamic energy-quality management in approximate SRAMs.

Approximate SRAMs With Dynamic Energy-Quality Management

Data-pre-charged dynamic logic, also known as data-driven dynamic logic (D3L), is very efficient when low-power constraints are mandatory. Differently from conventional dynamic domino logic, which exploits a clock signal, D3L uses a subset of the input data signals for pre-charging the dynamic node, thus avoiding the clock distribution network. Power consumption is significantly reduced, but the pre-charge propagation path delay affects the speed performances and limits the energy-delay product (EDP) improvements. This study presents a new dynamic logic named split-path D3L (SPD3L) that overcomes the speed limitations of D3L. When applied to a 16 times 16 bit booth multiplier realised with STMicroelectronics 65 nm IV CMOS technology, the proposed technique leads to an EDP 25 and 30% lower than standard dynamic domino logic and conventional D3L counterparts, respectively.

Low-power split-path data-driven dynamic logic

Dynamic CMOS gates are widely exploited in high-performance designs because of their speed. However, they suffer from high noise sensitivity. The main reason for this is the sub-threshold leakage current flowing through the evaluation network. This problem becomes more and more severe with continuous scaling of the technology. A new circuit technique for increasing the noise tolerance of dynamic CMOS gates is studied. A comparison with previously reported schemes is presented. Simulations proved that, when 90 nm CMOS technology is used to realise wide fan-in gates, the proposed design technique can achieve the highest level of noise robustness. A 16 bits OR gate designed as proposed here shows a maximum unity noise gain of 675 mV, a computational delay of ∼115 ps and an energy dissipation of ∼33 fJ. Moreover, at the parity of energy-delay product (EDP), the novel approach achieves a noise robustness 10% higher than the most efficient technique existing in the literature, whereas, at the parity of noise robustness, it exhibits an EDP 33% lower.

Fabio Frustaci

Papers

SRAM for Error-Tolerant Applications With Dynamic Energy-Quality Management in 28 nm CMOS

Analytical Delay Model Considering Variability Effects in Subthreshold Domain

Approximate SRAMs With Dynamic Energy-Quality Management

Low-power split-path data-driven dynamic logic

High-performance noise-tolerant circuit techniques for CMOS dynamic logic