CEA-Leti’s chief scientist has issued a forward-looking call to action for the microelectronics industry to create a radically new, digital-communication architecture for the internet of things (IoT) in which “a great deal of analytics processing occurs at the edge and at the end devices instead of in the Cloud.”
Delivering a keynote presentation at the kickoff of the 2018 IEEE International Solid-State Circuits Conference (ISSCC) 2018 in San Francisco, Barbara De Salvo said this architecture will include human-brain inspired hardware coupled to new computing paradigms and algorithms that “will allow for distributed intelligence over the whole IoT network, all-the-way down to ultralow-power end-devices.”
“We are entering a new era where artificial-intelligence systems are … shaping the future world,” said De Salvo, who also is Leti’s scientific director. “With the end of Moore’s Law in sight, transformative approaches are needed to address the enduring power-efficiency issues of traditional computing architectures.”
The potential efficiencies of processing data at the edge of networks by small computers located near IoT-connected device rather than at distant data centers or the cloud are increasingly cited as long-term goals for the IoT. But challenges remain: IoT battery-powered devices lack both processing power to analyze the data they receive and a power source that would support data processing.
To break through these barriers, De Salvo called for a “holistic research approach to the development of low-power architectures inspired by the human brain, where process development and integration, circuit design, system architecture and learning algorithms are simultaneously optimized.” She envisions a future in which optimized neuromorphic hardware will be implemented as a highly promising solution for future ultralow-power cognitive systems that extend well beyond the IoT.
“Emerging technologies such as advanced CMOS, 3D technologies, emerging resistive memories, and silicon photonics, coupled with novel brain-inspired paradigms, such as spike-coding and spike-time-dependent-plasticity, have extraordinary potential to provide intelligent features in hardware, approaching the way knowledge is created and processed in the human brain,” she said.
De Salvo’s presentation, “Brain-Inspired Technologies: Towards Chips that Think,” included summaries of key research findings in a variety of fields that will play a role in developing brain-inspired technologies for computing and data-handling requirements of a “hyperconnected” world.
Human Brain Research
Tracing major discoveries about how the brain works, De Salvo cited the emergence of connectionism, novel neuroimaging techniques and the functioning of neural networks, which may provide models for brain-inspired technologies.
“The large-scale neuronal networks of the brain are arranged globally as hierarchical modular networks, with dense modules at the local level (cellular circuits, laminar compartments) that are encapsulated in increasingly larger modules (cortical columns, areas and whole lobes), but with very sparse overall connectivity,” she said. “Such a topology fundamentally enhances the brain’s dynamic stability and information-processing abilities.
“An important research target will be to understand how the three-dimensional organization of brain cells, neurons and glial cells, connected in networks within the layers of the brain cortex, are responsible for the emergence of genetically determined elementary operations,” De Salvo said.
Hyperconnectivity and Deep Learning
She also noted that the convergence of miniaturization, wireless connectivity, increased data-storage capacity and data analytics has positioned the IoT at the epicenter of profound social, business and political changes.
Also cited were significant gains in the performance and applications of machine learning, driven by vast data storage in images, videos, audio and text files available across the internet. These gains, in turn, have been essential to the dramatic improvement of learning and training approaches and algorithms, as well as the increased computational power of computers, including parallel computing for neural network processing, which has compensated for the slowing down of Moore’s law below the 10 nm node. Deep learning is the most popular machine-learning field.
Approaching the Edge
“Bringing intelligence to the edge or to end-devices means doing useful processing of the data as close to the collection point as possible, and allowing systems to make some operational decisions locally, possibly semi-autonomously,” De Salvo explained.
Locally controlling real-time distance learning is essential for many applications, from landing drones to navigating driverless cars.
“The delay caused by the round-trip to the Cloud could lead to disastrous or even fatal results,” she said. “Privacy will require that key data not leave the user’s device, while transmission of high-level information, generated by local neural-network algorithms, will be authorized.”
Raw videos generated by millions of cameras will have to be locally analyzed to limit bandwidth issues and communication costs. For all these reasons, new concepts and technologies that can bring artificial intelligence closer to the edge and end-devices are in high demand.
“The primary design goal in distributed applications covering several levels of hierarchy (similar to what happens in the brain), is to find a global optimum between performance and energy consumption,” De Salvo said. “This imperative requires a holistic research approach, where the technology stack (from device to applications) is redesigned.”
This process is underway. Companies are addressing embedded applications by developing specialized edge platforms that can execute machine-learning algorithms on embedded hardware. Impressive power improvements (down to a few watts) have been achieved exploiting Moore’s law: pushing the FinFET technology down to the 7 nm node and by hardware-software co-optimization. To optimize energy efficiency in mobile devices, several research groups have focused on hardware designs of convolutional neural network (CNN) accelerators. De Salvo noted that off-chip storage devices, such as DRAMs, significantly increase power consumption, but that mobile-oriented applications (keyword spotting and face detection) have been demonstrated with a low-power, programmable, deep-learning accelerator that consumes less than 300 µW.
‘Extremely Critical’ Power Requirements
Some fixes to challenges are still in the discussion stage. For example, De Salvo noted that bringing intelligence into low-power, IoT-connected, end-devices that support applications such as habitat monitoring and medical surveillance is significantly more difficult than bringing it to traditional networked mobile devices at the edge.
“Most connected end devices are wireless sensor nodes containing microcontrollers, wireless transceivers, sensors and actuators,” she said. “The power requirement for these systems is extremely critical – less than 100μW for normal workloads – as these devices often operate using energy-harvesting sources or a single battery for several years.”
De Salvo said scientists inspired by the human brain, whose computing performance and efficiency still remain unmatched, are pursuing a radically different approach to neuromorphic systems.
“It consists in implementing bio-inspired architectures in optimized neuromorphic hardware to provide direct one-to-one mapping between the hardware and the learning algorithm running on it,” she said. These architectures include spike coding, which encodes neuron values as pulses or spikes rather then analog or digital values, and spike-timing-dependent-plasticity, a bio-inspired algorithm that enables unsupervised learning.
3D Technologies: Key Enablers of Neuromorphic Hardware
De Salvo said the human brain’s intelligence and efficiency are strongly linked to its extremely dense 3D interconnectivity. For example, there are approximately 10,000 synapses per neuron, and billions of neurons in the human brain cortex.
“The hierarchical structure in the cortex follows specific patterns, through vertical arrangements or µcolumns, where local data flow on subcortical specialized structures, and laminar interconnections, which foster inter-area communications and build the hierarchy.
“Based on these considerations, it is clear that emerging 3D technologies, such as through-silicon vias and 3D monolithic integration, also called CoolCubeTM, will be a key enabler of efficient neuromorphic hardware,” she said.
Outlining silicon technologies that will be vital to creating brain-inspired hardware, De Salvo also cited resistive memories or ReRAM, fully depleted silicon on insulator (FDSOI) and silicon photonics.
“Thanks to its suitability for low-power design, FDSOI technology is a great candidate for neuromorphic hardware,” she said. In deep-learning architectures, high-performance, reconfigurable digital processors based on 28 nm FDSOI have shown power consumption in the range of 50 mW, a power efficiency achieved by introducing optimized data-movement strategy and exploiting FDSOI back-biasing strategies. De Salvo also noted that a large-scale, multi-core, neuromorphic processor called Dynap-SEL, also based on 28 nm FDSOI, recently was demonstrated.
The Road Ahead
De Salvo concluded her presentation about the future and potential of brain-inspired hardware, AI and edge computing by listing the technological challenges on the road to that goal. But she left the audience with one final prediction.
“New materials to interface devices with living cells and tissues, new design architectures for lowering power consumption, data extraction and management at the system level, and secured communications are the next domains that will experience intense development,” she said. “Brain-inspired implantable microdevices, acting as intelligent neuroprostheses, and bio-hybrid systems represent the new era of cross-disciplinary brain-repair strategies, where biological and engineered solutions will complement each other, probably mediated by artificial intelligence.”