[HTML][HTML] Survey of deep learning accelerators for edge and emerging computing
The unprecedented progress in artificial intelligence (AI), particularly in deep learning
algorithms with ubiquitous internet connected smart devices, has created a high demand for …
algorithms with ubiquitous internet connected smart devices, has created a high demand for …
Trending IC design directions in 2022
For the non-stop demands for a better and smarter society, the number of electronic devices
keeps increasing exponentially; and the computation power, communication data rate, smart …
keeps increasing exponentially; and the computation power, communication data rate, smart …
A CMOS-integrated spintronic compute-in-memory macro for secure AI edge devices
YC Chiu, WS Khwa, CS Yang, SH Teng, HY Huang… - Nature …, 2023 - nature.com
Artificial intelligence edge devices should offer high inference accuracy and rapid response
times, as well as being energy efficient. Ensuring the security of these devices against …
times, as well as being energy efficient. Ensuring the security of these devices against …
A charge domain SRAM compute-in-memory macro with C-2C ladder-based 8-bit MAC unit in 22-nm FinFET process for edge inference
Compute-in-memory (CiM) is one promising solution to address the memory bottleneck
existing in traditional computing architectures. However, the tradeoff between energy …
existing in traditional computing architectures. However, the tradeoff between energy …
8-b precision 8-Mb ReRAM compute-in-memory macro using direct-current-free time-domain readout scheme for AI edge devices
JM Hung, TH Wen, YH Huang… - IEEE Journal of Solid …, 2022 - ieeexplore.ieee.org
Compute-in-memory (nvCIM) macros based on non-volatile memory make it possible for
artificial intelligence (AI) edge devices to perform energy-efficient multiply-and-accumulate …
artificial intelligence (AI) edge devices to perform energy-efficient multiply-and-accumulate …
33.4 A 28nm 2Mb STT-MRAM computing-in-memory macro with a refined bit-cell and 22.4-41.5 TOPS/W for AI inference
Emerging non-volatile memory-based computing-in-memory (CIM) is an excellent fit for
resource-constrained edge-AI devices [1–6]. MRAM-CIM macros for MAC operations, at …
resource-constrained edge-AI devices [1–6]. MRAM-CIM macros for MAC operations, at …
A 28 nm 16 kb bit-scalable charge-domain transpose 6T SRAM in-memory computing macro
This article presents a compact, robust, and transposable SRAM in-memory computing
(IMC) macro to support feed forward (FF) and back propagation (BP) computation within a …
(IMC) macro to support feed forward (FF) and back propagation (BP) computation within a …
A survey of MRAM-centric computing: From near memory to in memory
Conventional von Neumann architecture suffers from bottlenecks in computing performance
and power consumption due to frequent data exchange between memory and processing …
and power consumption due to frequent data exchange between memory and processing …
Power-delay area-efficient processing-in-memory based on nanocrystalline Hafnia ferroelectric field-effect transistors
Ferroelectric field-effect transistors (FeFETs) have attracted enormous attention for low-
power and high-density nonvolatile memory devices in processing-in-memory (PIM) …
power and high-density nonvolatile memory devices in processing-in-memory (PIM) …
A 22 nm Floating-Point ReRAM Compute-in-Memory Macro Using Residue-Shared ADC for AI Edge Device
Artificial intelligence (AI) edge devices increasingly require the enhanced accuracy of
floating-point (FP) multiply-and-accumulate (MAC) operations as well as nonvolatile on-chip …
floating-point (FP) multiply-and-accumulate (MAC) operations as well as nonvolatile on-chip …