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In modern ICs, the trend of integrating more on-chip memories on a die has led SRAMs to account for a large fraction of total area and energy of a chip. Therefore, designing memories with dynamic voltage scaling (DVS) capability is important since significant active as well as leakage power savings can be achieved by voltage scaling. However, optimizing circuit operation over a large voltage range is not trivial due to conflicting trade-offs of low-voltage (moderate and weak inversion) and high-voltage (strong inversion) transistor characteristics. Specifically, low-voltage operation requires various assist circuits for functionality which might severely impact high-voltage performance. Reconfigurable assist circuits provide the necessary adaptability for circuits to adjust themselves to the requirements of the voltage range that they are operating in. This paper presents a 64 kb reconfigurable SRAM fabricated in 65 nm low-power CMOS process operating from 250 mV to 1.2 V. This wide supply range was enabled by a combination of circuits optimized for both subthreshold and above-threshold regimes and by employing hardware reconfigurability. Three different write-assist schemes can be selectively enabled to provide write functionality down to very low voltage levels while preventing excessive power overhead. Two different sense-amplifiers are implemented to minimize sensing delay over a large voltage range. A prototype test chip is tested to be operational at 20 kHz with 250 mV supply and 200 MHz with 1.2 V supply. Over this range leakage power scales by more than 50 X and a minimum energy point is achieved at 0.4 V with less than 0.1 pJ/bit/access.

Addressing the challenges of extreme scale computing requires holistic design of new programming models and systems that support those models. This paper discusses the Angstrom processor, which is designed to support a new Self-aware Computing (SEEC) model. In SEEC, applications explicitly state goals, while other systems components provide actions that the SEEC runtime system can use to meet those goals. Angstrom supports this model by exposing sensors and adaptations that traditionally would be managed independently by hardware. This exposure allows SEEC to coordinate hardware actions with actions specified by other parts of the system, and allows the SEEC runtime system to meet application goals while reducing costs (e.g., power consumption).

SRAM IS THE most common embedded-memory option for CMOS ICs. As the supply voltage of lowpower ICs decreases, it must remain compatible with the operating conditions. At the same time, increasingly parallel architectures of such low-power systems demand more on-chip cache to effectively share information across parallel processing units. Finally, supply voltage scaling improves the energy consumed by SRAM and dramatically reduces its leakage power.Achieving low-voltage operation in SRAM faces a confluence of challenges, originating from process variation, and related to bit cell stability, sensing, architecture, and efficient CAD methodologies. The trend toward increased quantity of embedded SRAM in scaled technology compounds the specific need of SRAM in low-power systems. Integrating more memory on chip provides an effective means to use silicon because of memory's lower power density, layout regularity, and performance and power benefits from reduced off-chip bandwidth. As a result, the everincreasing integration of embedded SRAM continues. 2 By employing some of the design innovations discussed in this article, this memory design minimizes energy per access and achieves a 50Â reduction in leakage power by trading off performance. Indeed, low-power systems benefit from SRAMs that function at very low voltage in the state of the art, but such design solutions of lowvoltage SRAM significantly impact area and performance. 3 Reducing this area overhead and further improving the metrics of energy per accessed bit and leakage power will enable new opportunities for low-power electronics in mobile platforms. Wearable electronics, portable medical monitors, and implantable medical devices are some of the applications requiring the storage of significant quantities of information (e.g., patient data), low-access energy caches, and a long operating lifetime from a battery.In this article, we discuss the challenges to embedded-SRAM design, with particular emphasis on the factors that limit the minimum operating supply voltage V min . We also explore various design solutions and discuss open areas of investigation. ChallengesThe workhorse of embedded memory is SRAM based on the 6T (six-transistor) cell, shown in Figure 2a. From this cell, a subarray is assembled by tiling memory cells into a grid with wordlines (WLs) running horizontally and bitlines (BLs) running vertically, as in Figure 3. Each memory cell is associated with one or more WLs and one or more BLs. Nominally, the bit cell supplies and well biases are globally connected to static voltage sources. During read, the WL voltage V WL is raised, and the memory cell discharges either BLT (bitline true) or BLC (bitline complement), depending on the stored data on nodes Q and bQ. A sense amplifier converts the differential signal to a logic-level output. Then, at the end of the read cycle, the BLs return to the positive supply rail. During write, V WL is raised and the BLs are forced to Future Landscape of Embedded MemoriesEditor's note: SRAMs capable of operating a...

Circuits such as logic cells, static random access memories, analog-digital converters and dc-dc converters can be used as building blocks for applications that can function efficiently over a wide range of supply voltages.ABSTRACT | Energy efficiency of electronic circuits is a critical concern in a wide range of applications from mobile multimedia to biomedical monitoring. An added challenge is that many of these applications have dynamic workloads. To reduce the energy consumption under these variable computation requirements, the underlying circuits must function efficiently over a wide range of supply voltages. This paper presents voltage-scalable circuits such as logic cells, SRAMs, ADCs, and dc-dc converters. Using these circuits as building blocks, two different applications are highlighted. First, we describe an H.264/AVC video decoder that efficiently scales between QCIF and 1080p resolutions, using a supply voltage varying from 0.5 V to 0.85 V. Second, we describe a 0.3 V 16-bit microcontroller with on-chip SRAM, where the supply voltage is generated efficiently by an integrated dc-dc converter. Fig. 5. Trend in minimum energy point of a 32 b adder with process scaling using predictive models [31].

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