Growing evidence from primate neurophysiology and modeling indicates that in reaction
time tasks, a perceptual choice is made when the firing rate of a selective cortical neural …

Battery-powered tiny-AI edge devices require large-capacity nonvolatile compute-in-memory
(nvCIM), with multibit input (IN), weight (W), and output (OUT) precision to support complex …

Although mammals of different species have different sleep patterns, brief sleep–wake
transitions commonly are observed across species and appear to occur randomly throughout the …

Embedded nonvolatile memory (NVM) and computing-in-memory (CIM) are significantly
reducing the latency (t MAC ) and energy consumption (E MAC ) of multiply- and-accumulate (…

Recent SRAM-based computation-in-memory (CIM) macros enable mid-to-high precision
multiply-and-accumulate (MAC) operations with improved energy efficiency using ultra-small/…

Non-volatile computing-in-memory (nvCIM) architecture can reduce the latency and energy
consumption of artificial intelligence computation by minimizing the movement of data …

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been
collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-…

Nonvolatile computing-in-memory (nvCIM) can improve the latency (t AC ) and energy-efficiency
(EF MAC ) of tiny AI edge devices performing multiply-and-accumulate (MAC) …

Advanced AI edge chips require multibit input (IN), weight (W), and output (OUT) for CNN
multiply-and-accumulate (MAC) operations to achieve an inference accuracy that is sufficient …

The development of small, energy-efficient artificial intelligence edge devices is limited in
conventional computing architectures by the need to transfer data between the processor and …