TinyVers: A Tiny Versatile System-on-chip with State-Retentive eMRAM for ML Inference at the Extreme Edge

Extreme edge devices or Internet-of-thing nodes require both ultra-low power always-on processing as well as the ability to do on-demand sampling and processing. Moreover, support for IoT applications like voice recognition, machine monitoring, etc., requires the ability to execute a wide range of ML workloads. This brings challenges in hardware design to build flexible processors operating in ultra-low power regime. This paper presents TinyVers, a tiny versatile ultra-low power ML system-on-chip to enable enhanced intelligence at the Extreme Edge. TinyVers exploits dataflow reconfiguration to enable multi-modal support and aggressive on-chip power management for duty-cycling to enable smart sensing applications. The SoC combines a RISC-V host processor, a 17 TOPS/W dataflow reconfigurable ML accelerator, a 1.7 $\mu$W deep sleep wake-up controller, and an eMRAM for boot code and ML parameter retention. The SoC can perform up to 17.6 GOPS while achieving a power consumption range from 1.7 $\mu$W-20 mW. Multiple ML workloads aimed for diverse applications are mapped on the SoC to showcase its flexibility and efficiency. All the models achieve 1-2 TOPS/W of energy efficiency with power consumption below 230 $\mu$W in continuous operation. In a duty-cycling use case for machine monitoring, this power is reduced to below 10 $\mu$W.Comment: Accepted in IEEE Journal of Solid-State Circuit

Exploration and Design of Low-Energy Logic Cells for 1 kHz Always-on Systems

© 2019 EDAA. A standard cell library targeting always-on operation at 1 kHz is designed at circuit-level. This paper proposes a design methodology to achieve robust operation with minimum energy. Such minimum energy per operation for always-on systems is achieved by one specific supply and threshold voltage VTh combination. As VTh is discrete in a practical bulk technology, this minimum can however not be achieved through simple voltage tuning. In the considered 90 nm CMOS technology, VTh is too low resulting in leakage dominated systems and preventing from attaining the minimum energy point in subthreshold. Three circuit techniques are optimally combined to fight leakage: stacking, reverse body biasing and optimal transistor dimensioning relying on second order effects of the dimensions on VTh. They jointly allow logic gates to achieve the best balance between dynamic and leakage power. Moreover, the paper presents modified flip-flop topologies that also reliably operate at 0.27 V along with the gates. Benefits of improved logic gates and flip-flops are demonstrated on a small always-on feature-extraction system calculating running average and variance on a 1 Ksample/s data stream. The resulting system consumes 162 pW in simulation, or two orders of magnitude less when compared to a commercial library at its 1 V nominal voltage, or 1 order of magnitude less when compared to the commercial library at the same 0.27 V operating voltage.status: Published onlin

A procedural method to predictively assess power-quality trade-offs of circuit-level adaptivity in IoT systems

The constant miniaturization of IoT sensor nodes requires a continuous reduction in battery sizes, leading to more stringent needs in terms of low-power operation. Over the past decades, an extremely large variety of techniques have been introduced to enable such reductions in power consumption. Many involve some form of offline reconfigurability (OfC), i.e., the ability to configure the node before deployment, or online adaptivity (OnA), i.e., the ability to also reconfigure the node during run time. Yet, the inherent design trade-offs usually lead to ad hoc OnA and OfC, which prevent assessing the varying benefits and costs each approach implies before investing in implementation on a specific node. To solve this issue, in this work, we propose a generic predictive assessment methodology that enables us to evaluate OfC and OnA globally, prior to any design. Practically, the methodology is based on optimization mathematics, to quickly and efficiently evaluate the potential benefits and costs from OnA relative to OfC. This generic methodology can, thus, determine which type of solution will consume the least amount of power, given a specific application scenario, before implementation. We applied the methodology to three adaptive IoT system studies, to demonstrate the ability of the introduced methodology, bring insights into the adaptivity mechanics, and quickly optimize the OfC–OnA adaptivity, even under scenarios with many adaptivity variables.Peer reviewe

Instinct-driven dynamic hardware reconfiguration: evolutionary algorithm optimized compression for autonomous sensory agents

© 2017 ACM. Advancement in miniaturization of autonomous sensory agents can play a profound role in many applications such as the exploration of unknown environments, however, due to their miniature size, power limitations poses a severe challenge. In this paper, and inspired from biological instinctive behaviour, we introduce an instinct-driven dynamic hardware reconfiguration design scheme using evolutionary algorithms on behaviour trees. Moreover, this scheme is projected on an application scenario of autonomous sensory agents exploring an inaccessible dynamic environment. In this scenario, agent's compression behaviour-introduced as an instinct-is critical due to the limited energy available on the agents. This emphasises the role of optimization of agents resources through dynamic hardware reconfiguration. In that regard, the presented approach is demonstrated using two compression techniques: Zeroorder hold and Wavelet compression. Behavioural and hardwarebased power models of these techniques, integrated with behaviour trees (BT), are implemented to facilitate off-line learning of the optimum on-line behaviour, thus, facilitating dynamic reconfiguration of agents hardware.status: publishe

Flexible and Self-adaptive Sense-and-Compress for sub-microWatt always-on sensory recording

© 2018 IEEE. Miniaturized sensory systems for IoT applications experience a severe power burden from their wireless link and/or embedded storage system. Compressive sensing techniques target data compression before storage and transmission to save power, while minimizing information loss. This work proposes a self-adaptive sense-and-compress system, which consumes only 45-884n W while continuously recording and compressing signals with a bandwidth up to 5kHz. The flexible system uses a combination of off-line Evolutionary Algorithms, and on-line self-adaptivity to constantly adapt to the incoming sensory data statistics, and the current application quality requirements. The 0.27mm2 sense-and-compress interface is integrated in a 65nm CMOS technology, together with an on-board temperature sensor, or can interface with any external sensor. The scalable, self-adaptive system is moreover heavily optimized for low-power and low-leakage, resulting in a tiny, efficient, yet flexible interface allowing always-on sensory monitoring, while consuming 2.5X less power compared to the current State-of-the-Art.status: publishe

Flexible and self-adaptive sense-and-compress for sub-microWatt always-on sensory recording

Miniaturized sensory systems for IoT applications experience a severe power burden from their wireless link and/or embedded storage system. Compressive sensing techniques target data compression before storage and transmission to save power, while minimizing information loss. This work proposes a self-adaptive sense-and-compress system, which consumes only 45-884n W while continuously recording and compressing signals with a bandwidth up to 5kHz. The flexible system uses a combination of off-line Evolutionary Algorithms, and on-line self-adaptivity to constantly adapt to the incoming sensory data statistics, and the current application quality requirements. The 0.27mm2 sense-and-compress interface is integrated in a 65nm CMOS technology, together with an on-board temperature sensor, or can interface with any external sensor. The scalable, self-adaptive system is moreover heavily optimized for low-power and low-leakage, resulting in a tiny, efficient, yet flexible interface allowing always-on sensory monitoring, while consuming 2.5X less power compared to the current State-of-the-Art

Flexible, Self-Adaptive Sense-and-Compress SoC for Sub-microWatt Always-On Sensory Recording

We present a 5-sensor, fully integrated sensing system with interchangeable sensors and programmable configuration to create a sub-microWatt multisensor node that can tackle a wide range of sensing applications. Furthermore, the sensor node is capable of autonomously adapting its configuration to the application requirements hence minimizing system power. Such self-reconfiguration is enabled at low overhead by developing an automated offline optimization strategy, in combination with an autonomous embedded configuration controller, using the concept of behavioral trees (BTs). The resulting fully integrated platform consumes a maximum of 321 nW when sampling at 500 Hz and 3025 nW at 8 kHz. Furthermore, we demonstrate the end-to-end autonomous optimization flow for two different applications exploiting different sensors: 1) human activity recognition using accelerometers and 2) machine listening using a microphone. Both use cases demonstrate that the introduced system and methodology reduces the power by more than a factor 2 without losing significant application detection accuracy

Evolving hardware instinctive behaviors in resource-scarce agent swarms exploring hard-to-reach environments

This work introduces a novel adaptation framework to energy-eciently adapt small-sized circuits operating under scarce resources in dynamic environments, as autonomous swarm of sensory agents. This framework makes it possible to optimally congure the circuit based on three key mechanisms: (a) an o-line optimization phase relying on R2 indicator based Evolutionary Multi-objective Optimization Algorithm (EMOA), (b) an on-line phase based on hardware instincts and (c) the possibility to include the environment in the optimization loop. Specically, the evolutionary algorithm is able to simultaneously determine an optimal combination of static settings and dynamic instinct for the hardware, considering highly dynamic environments. The instinct is then run on-line with minimal on-chip resources so that the circuit eciently react to environmental changes. This framework is demonstrated on an ultrasonic communication system between energy-scarce wireless nodes. The proposed approach is environment-adaptive and enables power savings up to 45% for the same performance on the considered case studies