Power Efficient Data-Aware SRAM Cell for SRAM-Based FPGA Architecture

The design of low-power SRAM cell becomes a necessity in today\u27s FPGAs, because SRAM is a critical component in FPGA design and consumes a large fraction of the total power. The present chapter provides an overview of various factors responsible for power consumption in FPGA and discusses the design techniques of low-power SRAM-based FPGA at system level, device level, and architecture levels. Finally, the chapter proposes a data-aware dynamic SRAM cell to control the power consumption in the cell. Stack effect has been adopted in the design to reduce the leakage current. The various peripheral circuits like address decoder circuit, write/read enable circuits, and sense amplifier have been modified to implement a power-efficient SRAM-based FPGA

Low power techniques for video compression

This paper gives an overview of low-power techniques proposed in the literature for mobile multimedia and Internet applications. Exploitable aspects are discussed in the behavior of different video compression tools. These power-efficient solutions are then classified by synthesis domain and level of abstraction. As this paper is meant to be a starting point for further research in the area, a lowpower hardware & software co-design methodology is outlined in the end as a possible scenario for video-codec-on-a-chip implementations on future mobile multimedia platforms

Palmo : a novel pulsed based signal processing technique for programmable mixed-signal VLSI

In this thesis a new signal processing technique is presented. This technique exploits the use of pulses as the signalling mechanism. This Palmo 1 signalling method applied to signal processing is novel, combining the advantages of both digital and analogue techniques. Pulsed signals are robust, inherently low-power, easily regenerated, and easily distributed across and between chips. The Palmo cells used to perform analogue operations on the pulsed signals are compact, fast, simple and programmable

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20 μm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1