innovation for industry
SAN FRANCISCO – Feb. 14, 2018 – Leti’s chief scientist today issued a forward-looking call to action for the microelectronics industry to create a radically new, digital-communication architecture for the Internet of Things 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 ISSCC 2018, 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 ultra-low 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.”
“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.
“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.
She also noted that the convergence of miniaturization, wireless connectivity, increased data-storage capacity, and data analytics, has positioned the Internet of Things at the epicenter of profound social, business and political changes.
“With billions of easy-access and low-cost connected devices, the world has entered the era of hyperconnectivity, enabling people and machines to interact in a symbiotic way (anytime, anywhere) with both the physical and cyber worlds,” De Salvo said. “AI has been at the center of this revolution.”
“Today, for tasks such as image or speech recognition, machine-learning applications are equaling or even surpassing expert human performance,” De Salvo said. “Other tasks considered as extremely difficult in the past, such as natural language comprehension or complex games, have been successfully tackled.”
“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. Controlling real-time distance learning locally 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.”
“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.
“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 to build the hierarchy. Based on these considerations, it is clear that emerging 3D technologies (as through-silicon or 3D monolithic, also called CoolCubeTM) will be a key enabler of efficient neuromorphic hardware,” she said.
Outlining silicon technologies that will be vital to creating brain-spired hardware, De Salvo also cited, resistive memories or ReRAM, ,Fully Depleted Silicon on Insulator 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 28nm FDSOI have shown power consumption in the range of 50mW, 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 28nm FDSOI, recently was demonstrated.
De Salvo concluded her optimistic 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.”
CEA is a French government-funded technological research organisation in four main areas: low-carbon energies, defense and security, information technologies and health technologies. A prominent player in the European Research Area, it is involved in setting up collaborative projects with many partners around the world.