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DTSTAMP:20231009T143046Z
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DTSTART;TZID=America/Chicago:20231005T133000
DTEND;TZID=America/Chicago:20231005T143000
DESCRIPTION:This presentation will highlight some of the emerging challenge
 s and opportunities for sub-18Å process machine learning and AI technolog
 ies in the rapidly evolving IoT industry. With Moore’s law process techn
 ology scaling well into the nano-scale regime\, future SoC platforms rangi
 ng from high performance cloud servers to ultra-low-power edge devices wil
 l demand advanced AI capabilities and energy-efficient deep neural network
 s. New and emerging IoT markets for autonomous vehicles\, drones\, and wea
 rables require even higher\nperformance at much lower cost while reducing 
 energy consumption. Some of the prominent barriers to designing high perfo
 rmance and energy-efficient AI processors and SoCs in the sub-18Å technol
 ogy nodes will be outlined. New paradigm shifts necessary for integrating 
 special-purpose machine learning accelerators into next-generation SoCs wi
 ll be explored. Emerging trends in SoC circuit design for machine learning
  and deep neural networks\, specialized accelerators for digital and analo
 g in-memory and near-memory computing\, reconfigurable multi-precision mat
 rix multipliers\, ultra-low-voltage logic\, memory and clocking circuits\,
  AI inference accelerators including binary neural networks\nand associate
 d on-chip interconnect fabric circuits are described. Future braininspired
  neuromorphic computing circuit design challenges and technologies will al
 so be reviewed. Specific chip design examples and case studies supported b
 y silicon measurements and trade-offs will be discussed.\n\nSpeaker(s): Ra
 m Krishnamurthy\, \n\nVirtual: https://events.vtools.ieee.org/m/374506
LOCATION:Virtual: https://events.vtools.ieee.org/m/374506
ORGANIZER:sfpietri@gmail.com
SEQUENCE:20
SUMMARY:Feed Your Mind - HP Energy Efficient CiM and AI accelerators for su
 b-18Å Technologies - R. Krishnamurthy
URL;VALUE=URI:https://events.vtools.ieee.org/m/374506
X-ALT-DESC:Description: <br /><p>This presentation will highlight some of t
 he emerging challenges and opportunities for sub-18&Aring\; process machin
 e learning and AI technologies in the rapidly evolving IoT industry. With 
 Moore&rsquo\;s law process technology scaling well into the nano-scale reg
 ime\, future SoC platforms ranging from high performance cloud servers to 
 ultra-low-power edge devices will demand advanced AI capabilities and ener
 gy-efficient deep neural networks. New and emerging IoT markets for autono
 mous vehicles\, drones\, and wearables require even higher<br />performanc
 e at much lower cost while reducing energy consumption. Some of the promin
 ent barriers to designing high performance and energy-efficient AI process
 ors and SoCs in the sub-18&Aring\; technology nodes will be outlined. New 
 paradigm shifts necessary for integrating special-purpose machine learning
  accelerators into next-generation SoCs will be explored. Emerging trends 
 in SoC circuit design for machine learning and deep neural networks\, spec
 ialized accelerators for digital and analog in-memory and near-memory comp
 uting\, reconfigurable multi-precision matrix multipliers\, ultra-low-volt
 age logic\, memory and clocking circuits\, AI inference accelerators inclu
 ding binary neural networks<br />and associated on-chip interconnect fabri
 c circuits are described. Future braininspired neuromorphic computing circ
 uit design challenges and technologies will also be reviewed. Specific chi
 p design examples and case studies supported by silicon measurements and t
 rade-offs will be discussed.&nbsp\;</p>
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