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Register a new userA Fully-Integrated 5mW, 0.8Gbps Energy-Efficient Chip-to-Chip Data Link for Ultra-Low-Power IoT End-Nodes in 65-nm CMOS
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The increasing complexity of Internet-of-Things (IoT) applications and near-sensor processing algorithms is pushing the computational power of low-power, battery-operated end-node systems. This trend also reveals growing demands for high-speed and energy-efficient inter-chip communications to manage the increasing amount of data coming from off-chip sensors and memories. While traditional micro-controller interfaces such as SPIs cannot cope with tight energy and large bandwidth requirements, low-voltage swing transceivers can tackle this challenge thanks to their capability to achieve several Gbps of the communication speed at milliwatt power levels. However, recent research on high-speed serial links focused on high-performance systems, with a power consumption significantly larger than the one of low-power IoT end-nodes, or on stand-alone designs not integrated at a system level. This paper presents a low-swing transceiver for the energy-efficient and low power chip-to-chip communication fully integrated within an IoT end-node System-on-Chip, fabricated in CMOS 65nm technology. The transceiver can be easily controlled via a software interface; thus, we can consider realistic scenarios for the data communication, which cannot be assessed in stand-alone prototypes. Chip measurements show that the transceiver achieves 8.46x higher energy efficiency at 15.9x higher performance than a traditional microcontroller interface such as a single-SPI.
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We present a power efficient clock-less fully asynchronous bit-serial Low Voltage Differential Signaling (LVDS) link with event-driven instant wake-up and self-sleep features, optimized for high speed inter-chip communication of asynchronous address-events between neuromorphic chips. The proposed LVDS link makes use of the Level-Encoded Dual-Rail (LEDR) representation and a token-ring architecture to encode and transmit data, avoiding the use of conventional large ClockData Recovery (CDR) modules with power-hungry DLL or PLL circuits. We implemented the LVDS circuits in a device fabricated with a standard 0.18 um CMOS process. The total silicon area used for such block is of 0.14 mm^2. We present experimental measurement results to demonstrate that, with a bit rate of 1.5 Gbps and an event width of 32-bit, the proposed LVDS link can achieve transmission event rates of 35.7 M Events/second with current consumption of 19.3 mA and 3.57 mA for receiver and transmitter blocks, respectively. Given the clock-less and instant on/off design choices made, the power consumption of the whole link depends linearly on the data transmission rate. We show that the current consumption can go down to sub-uA for low event rates (e.g., <1k Events/second), with a floor of 80 nA for transmitter and 42 nA for receiver, determined mainly by static off-leakage currents.
Emerging applications such as wireless sensing, position location, robotics, and many more are driven by the ultra-wide bandwidths available at millimeter-wave (mmWave) and Terahertz (THz) frequencies. The characterization and efficient utilization of wireless channels at these extremely high frequencies require detailed knowledge of the radio propagation characteristics of the channels. Such knowledge is developed through empirical observations of operating conditions using wireless transceivers that measure the impulse response through channel sounding. Today, cutting-edge channel sounders rely on several bulky RF hardware components with complicated interconnections, large parasitics, and sub-GHz RF bandwidth. This paper presents a compact sliding correlation-based channel sounder baseband built on a monolithic integrated circuit (IC) using 65 nm CMOS, implemented as an evaluation board achieving a 2 GHz RF bandwidth. The IC is the worlds first gigabit-per-second channel sounder baseband implemented in low-cost CMOS. The presented single-board system can be employed at both the transmit and receive baseband to study multipath characteristics and path loss. Thus, the singleboard implementation provides an inexpensive and compact solution for sliding correlation-based channel sounding with 1 ns multipath delay resolution.
Second-order nonlinear optical processes are used to convert light from one wavelength to another and to generate quantum entanglement. Creating chip-scale devices to more efficiently realize and control these interactions greatly increases the reach of photonics. Optical crystals and guided wave devices made from lithium niobate and potassium titanyl phosphate are typically used to realize second-order processes but face significant drawbacks in scalability, power, and tailorability when compared to emerging integrated photonic systems. Silicon or silicon nitride integrated photonic circuits enhance and control the third-order optical nonlinearity by confining light in dispersion-engineered waveguides and resonators. An analogous platform for second-order nonlinear optics remains an outstanding challenge in photonics. It would enable stronger interactions at lower power and reduce the number of competing nonlinear processes that emerge. Here we demonstrate efficient frequency doubling and parametric oscillation in a thin-film lithium niobate photonic circuit. Our device combines recent progress on periodically poled thin-film lithium niobate waveguidesand low-loss microresonators. Here we realize efficient >10% second-harmonic generation and parametric oscillation with microwatts of optical power using a periodically-poled thin-film lithium niobate microresonator. The operating regimes of this system are controlled using the relative detuning of the intracavity resonances. During nondegenerate oscillation, the emission wavelength is tuned over terahertz by varying the pump frequency by 100s of megahertz. We observe highly-enhanced effective third-order nonlinearities caused by cascaded second-order processes resulting in parametric oscillation. These resonant second-order nonlinear circuits will form a crucial part of the emerging nonlinear and quantum photonics platforms.
The next wave of on-device AI will likely require energy-efficient deep neural networks. Brain-inspired spiking neural networks (SNN) has been identified to be a promising candidate. Doing away with the need for multipliers significantly reduces energy. For on-device applications, besides computation, communication also incurs a significant amount of energy and time. In this paper, we propose Shenjing, a configurable SNN architecture which fully exposes all on-chip communications to software, enabling software mapping of SNN models with high accuracy at low power. Unlike prior SNN architectures like TrueNorth, Shenjing does not require any model modification and retraining for the mapping. We show that conventional artificial neural networks (ANN) such as multilayer perceptron, convolutional neural networks, as well as the latest residual neural networks can be mapped successfully onto Shenjing, realizing ANNs with SNNs energy efficiency. For the MNIST inference problem using a multilayer perceptron, we were able to achieve an accuracy of 96% while consuming just 1.26mW using 10 Shenjing cores.
Energy-constrained sensor nodes can adaptively optimize their energy consumption if a continuous measurement exists. This is of particular importance in scenarios of high dynamics such as energy harvesting or adaptive task scheduling. However, self-measuring of power consumption at reasonable cost and complexity is unavailable as a generic system service. In this paper, we present Eco, a hardware-software co-design enabling generic energy management on IoT nodes. Eco is tailored to devices with limited resources and thus targets most of the upcoming IoT scenarios. The proposed measurement module combines commodity components with a common system interfaces to achieve easy, flexible integration with various hardware platforms and the RIOT IoT operating system. We thoroughly evaluate and compare accuracy and overhead. Our findings indicate that our commodity design competes well with highly optimized solutions, while being significantly more versatile. We employ Eco for energy management on RIOT and validate its readiness for deployment in a five-week field trial integrated with energy harvesting.
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