Ultra-low-power SRAM design in high variability advanced CMOS

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Embedded SRAMs are a critical component in modern digital systems, and their role is preferentially increasing. As a result, SRAMs strongly impact the overall power, performance, and area, and, in order to manage these severely constrained trade-offs, they must be specially designed for target applications. Highly energy-constrained systems (e.g. implantable biomedical devices, multimedia handsets, etc.) are an important class of applications driving ultra-low-power SRAMs. This thesis analyzes the energy of an SRAM sub-array. Since supply- and threshold-voltage have a strong effect, targets for these are established in order to optimize energy. Despite the heavy emphasis on leakage-energy, analysis of a high-density 256x256 sub-array in 45nm LP CMOS points to two necessary optimizations: (1) aggressive supply-voltage reduction (in addition to Vt elevation), and (2) performance enhancement. Important SRAM metrics, including read/write/hold-margin and read-current, are also investigated to identify trade-offs of these optimizations. Based on the need to lower supply-voltage, a 0.35V 256kb SRAM is demonstrated in 65nm LP CMOS. It uses an 8T bit-cell with peripheral circuit-assists to improve write-margin and bit-line leakage. Additionally, redundancy, to manage the increasing impact of variability in the periphery, is proposed to improve the area-offset trade-off of sense-amplifiers, demonstrating promise for highly advanced technology nodes. Based on the need to improve performance, which is limited by density constraints, a 64kb SRAM, using an offset-compensating sense-amplifier, is demonstrated in 45nm LP CMOS with high-density 0.25[mu]m2 bit-cells.

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    ABSTRACT: Computational requirements in highly energy constrained applications are driving the need for ultra-low-power processors. In such devices SRAMs pose a primary energy limitation. This paper analyzes SRAM energy in practical applications using state-of-the-art power-management techniques. The design targets and array biasing for energy minimization are developed. Compared with generic logic, these are characterized by the important difference that SRAMs generally need to retain data. This restricts the use of power-gating for leakage elimination, and thus this paper considers the application of low-leakage data-retention biasing during the idle-mode. The resulting energy tradeoffs have important distinctions, and these are analyzed in the presence of practical variation levels.
    No preview · Article · Oct 2011 · IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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    ABSTRACT: Intelligent biomedical devices implies systems that are able to detect specific physiological processes in patients so that particular responses can be generated. This closed-loop capability can have enormous clinical value when we consider the unprecedented modalities that are beginning to emerge for sensing and stimulating patient physiology. Both delivering therapy (e.g., deep-brain stimulation, vagus nerve stimulation, etc.) and treating impairments (e.g., neural prosthesis) requires computational devices that can make clinically relevant inferences, especially using minimally-intrusive patient signals. The key to such devices is algorithms that are based on data-driven signal modeling as well as hardware structures that are specialized to these. This paper discusses the primary application-domain challenges that must be overcome and analyzes the most promising methods for this that are emerging. We then look at how these methods are being incorporated in ultra-low-energy computational platforms and systems. The case study for this is a seizure-detection SoC that includes instrumentation and computation blocks in support of a system that exploits patient-specific modeling to achieve accurate performance for chronic detection. The SoC samples each EEG channel at a rate of 600 Hz and performs processing to derive signal features on every two second epoch, consuming 9 µJ/epoch/channel. Signal feature extraction reduces the data rate by a factor of over 40×, permitting wireless communication from the patient's head while reducing the total power on the head by 14×.
    Preview · Article · Dec 2011 · Journal of Low Power Electronics and Applications