Researchers are studying how the functions of specific insect nervous systems resemble the functions performed by deterministic, probabilistic, volatile, and non-volatile memory and exploring how these functions could be recreated in high-performance, energy-efficient, silicon-based nanosystems for AI.
In one corner of the AI/ML world, NVIDIA does its best to convince anyone who wants to dip their toes into training that more raw power is the answer. In the other, CEA-Leti recently announced that Elisa Vianello, an Edge AI program coordinator, nabbed a €3 million grant from the European Research Council (ERC) to develop new edge AI systems inspired by insect nervous systems.
According to Vianello, one of the biggest challenges around bringing AI directly to IoT devices, which would allow them to make autonomous decisions based on comparing sensor input with trained data and decision trees, is that the current chip architectures waste up to 90% of their total energy consumption on moving data around, not processing it.
Because of this waste, IoT devices are either hindered in their AI capabilities or have to be physically tethered to a stable supply of power, which means they’re not nearly as flexible as many organizations would like. Doubly so for one proposed application, implantable medical diagnostic microchips, which would rely heavily on the user’s trust in the device’s reliability.
What’s the hiccup when it comes to small-scale devices? There just isn’t a type of memory that’s high-density, high-resolution, non-volatile, and endlessly endurant. Vianello says many industry labs and research centers have tried developing in-memory architectures in nano-scale, which use in-memory processing, but the results have been mixed at best. DRAM, for example, is volatile, which means that its contents are deleted when power is lost—a likely occurrence in many IoT settings. Non-volatile memory types, such as NVRAM, have dramatically improved endurances over the years; they’re still not completely infallible.
Vianello and her team will use the grant funding to research how the functions of specific insect nervous systems resemble the functions performed by deterministic, probabilistic, volatile, and non-volatile memory and then explore how those could be recreated in “high-performance, energy-efficient, silicon-based nanosystems.” Vianello says, “Crickets make accurate decisions based on sluggish, imprecise, and unreliable neurons and synapses in order to escape their predators. Looking closely at their biology, we identified a diversity of memory-like functions at play in their sensory and nervous systems. By combining these different functions, the cricket’s internal computing system achieves amazing performance and energy efficiency.”
For example, crickets have several sensors on their body, along with numerous local processing units in their abdomen, capable of continuous learning and decision-making without involving the central brain. Because it has a distributed computing system, it makes decisions faster—without having to transfer data from one place to another before processing it.
The goal is a hybrid synapse that can integrate more than one memory technology on a small, IoT-friendly scale, which would help navigate the flaws of each system and eliminate at least some of the power wasted in transferring data instead of processing it using AI/ML algorithms. Vianello’s team hopes to take small volumes of noisy data, such as what’s captured by video cameras, radar, or ECG devices, and enable the types of learning that aren’t possible using current IoT computing architecture.
New products driven by any new insights from Vianello’s team are likely many years away, and new paradigms are welcome in the rapidly-changing IoT world. With 46% of organizations ramping up their 5G adoption, multiple viable standards for edge computing, and more event-driven architectures being deployed on the edge, the demand for faster, more efficient, and more micro-scale IoT is already here.
Watafiti wanajifunza jinsi kazi za mfumo maalum wa neva ya wadudu zinavyofanana na kazi zinazofanywa na kumbukumbu za deterministic, probabilistic, volatile, na zisizo za volatile, na kugundua jinsi kazi hizi zinaweza kuundwa tena katika nano-mfumo wa utendaji wa juu, ufanisi wa nishati, na msingi wa silicon kwa AI. Katika kona moja ya ulimwengu wa AI, NVIDIA inafanya kila kitu ili kumshawishi mtu yeyote anayetaka kuzamisha vidole vya miguu katika mafunzo kwamba nguvu zaidi ni jibu. Elisa Vianello, mratibu wa programu ya Edge AI, amepata ruzuku ya € 3 milioni kutoka kwa Baraza la Utafiti la Ulaya (ERC) ili kuendeleza mifumo mpya ya AI ya Edge iliyoongozwa na mifumo ya neva ya wadudu. "Vianello anasema, ""moja ya changamoto kubwa karibu na kuleta AI moja kwa moja kwenye vifaa vya IoT, ambayo ingewawezesha kufanya maamuzi ya kujitegemea kulingana na kulinganisha pembejeo ya sensor na data ya mafunzo na miti ya uamuzi, ni kwamba usanifu wa sasa wa chip hutumia hadi 90% ya jumla ya matumizi yao ya nishati kusonga data karibu, sio kuifanya.""" Kwa sababu ya hasara hii, vifaa vya IoT vinazuiwa katika uwezo wao wa AI au lazima viunganishwe kimwili kwa usambazaji thabiti wa nguvu, ambayo inamaanisha sio rahisi kama mashirika mengi yangependa. Kwa mara mbili kwa ajili ya moja iliyopendekezwa maombi, implantable matibabu ya utambuzi microchips, ambayo ingekuwa kutegemea sana juu ya mtumiaji'mwenye imani katika kifaa' uaminifu. Ni nini hiccup linapokuja vifaa vidogo-scale? Kuna tu si aina ya kumbukumbu kwamba ni high-density, high-resolution, non-volatile, na endlessly enduring. Vianello anasema maabara nyingi za viwanda na vituo vya utafiti vimejaribu kuendeleza usanifu wa kumbukumbu katika kiwango cha nano, ambayo hutumia usindikaji wa kumbukumbu, lakini matokeo yamekuwa mchanganyiko. DRAM, kwa mfano, ni volatile, ambayo ina maana kwamba maudhui yake ni kufuta wakati nguvu ni kupotea - uwezekano tukio katika mazingira mengi IoT. Aina zisizo za kuteleza za kumbukumbu, kama vile NVRAM, zimeboresha sana uvumilivu kwa miaka mingi; bado hazina makosa kabisa. Vianello na timu yake watatumia fedha za ruzuku kutafiti jinsi kazi za mifumo maalum ya neva ya wadudu zinavyofanana na kazi zinazofanywa na kumbukumbu ya kuamua, uwezekano, kutoweza kutofautiana, na isiyoweza kutofautiana na kisha kuchunguza jinsi hizo zinaweza kuundwa tena katika nanosystems za utendaji wa juu, ufanisi wa nishati, na msingi wa silicon. Kwa kuzingatia biolojia yao, tuligundua tofauti za kazi kama za kumbukumbu zinazofanya kazi katika mifumo yao ya hisia na neva. Kwa kuchanganya kazi hizi tofauti, mfumo wa kompyuta wa ndani wa cricket hupata utendaji wa kushangaza na ufanisi wa nishati. kwa mfano, cricket zina sensorer kadhaa kwenye mwili wao, pamoja na vitengo vingi vya usindikaji wa ndani katika tumbo lao, uwezo wa kujifunza na kufanya maamuzi bila kujumuisha ubongo wa kati. Kwa sababu ina mfumo wa kompyuta uliosambazwa, hufanya maamuzi haraka <unk> bila kuhitaji kuhamisha data kutoka mahali moja hadi nyingine kabla ya kuisindika. Lengo ni synapse ya mseto ambayo inaweza kuunganisha teknolojia zaidi ya moja ya kumbukumbu kwa kiwango kidogo, cha IoT-kirafiki, ambayo itasaidia kuendesha mapungufu ya kila mfumo na kuondoa angalau baadhi ya nguvu iliyopotea katika kuhamisha data badala ya kuisindika kwa kutumia algorithms ya AI. Vianello anatarajia kuchukua kiasi kidogo cha data ya kelele, kama vile kile kilichochukuliwa na kamera za video, rada, au vifaa vya ECG, na kuwezesha aina za kujifunza ambazo haziwezekani kutumia usanifu wa sasa wa kompyuta ya IoT. Bidhaa mpya zinazoendeshwa na ufahamu wowote mpya kutoka kwa timu ya Vianello zinaweza kuwa miaka mingi mbali, na mifano mpya inakaribishwa katika ulimwengu wa IoT unaobadilika haraka. Kwa kuwa 46% ya mashirika yanaongeza kupitishwa kwa 5G, viwango vingi vinavyofaa vya kompyuta za makali, na usanifu zaidi unaoendeshwa na tukio unaotumiwa kwenye makali, mahitaji ya IoT ya haraka, ufanisi zaidi na ya kiwango kidogo tayari yupo hapa.