[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 …
Diana: An end-to-end hybrid digital and analog neural network soc for the edge
DIgital-ANAlog (DIANA), a heterogeneous multi-core accelerator, combines a reduced
instruction set computer-five (RISC-V) host processor with an analog in-memory computing …
instruction set computer-five (RISC-V) host processor with an analog in-memory computing …
A 28nm 16.9-300TOPS/W computing-in-memory processor supporting floating-point NN inference/training with intensive-CIM sparse-digital architecture
Computing-in-memory (CIM) has shown high energy efficiency on low-precision integer
multiply-accumulate (MAC)[1–3]. However, implementing floating-point (FP) operations …
multiply-accumulate (MAC)[1–3]. However, implementing floating-point (FP) operations …
A 95.6-TOPS/W deep learning inference accelerator with per-vector scaled 4-bit quantization in 5 nm
B Keller, R Venkatesan, S Dai, SG Tell… - IEEE Journal of Solid …, 2023 - ieeexplore.ieee.org
The energy efficiency of deep neural network (DNN) inference can be improved with custom
accelerators. DNN inference accelerators often employ specialized hardware techniques to …
accelerators. DNN inference accelerators often employ specialized hardware techniques to …
Deeploy: Enabling Energy-Efficient Deployment of Small Language Models on Heterogeneous Microcontrollers
With the rise of embodied foundation models (EFMs), most notably small language models
(SLMs), adapting Transformers for the edge applications has become a very active field of …
(SLMs), adapting Transformers for the edge applications has become a very active field of …
16.5 DynaPlasia: An eDRAM in-memory-computing-based reconfigurable spatial accelerator with triple-mode cell for dynamic resource switching
In-memory computing (IMC) processors show significant energy and area efficiency for deep
neural network (DNN) processing [1–3]. As shown in Fig. 16.5. 1, despite promising macro …
neural network (DNN) processing [1–3]. As shown in Fig. 16.5. 1, despite promising macro …
22.1 A 12.4 TOPS/W@ 136GOPS AI-IoT system-on-chip with 16 RISC-V, 2-to-8b precision-scalable DNN acceleration and 30%-boost adaptive body biasing
Emerging Artificial Intelligence-enabled Internet-of-Things (Al-loT) SoCs 1–4 for augmented
reality, personalized healthcare and nano-robotics need to run a large variety of tasks within …
reality, personalized healthcare and nano-robotics need to run a large variety of tasks within …
Benchmarking in-memory computing architectures
NR Shanbhag, SK Roy - IEEE Open Journal of the Solid-State …, 2022 - ieeexplore.ieee.org
In-memory computing (IMC) architectures have emerged as a compelling platform to
implement energy-efficient machine learning (ML) systems. However, today, the energy …
implement energy-efficient machine learning (ML) systems. However, today, the energy …
DynaPlasia: An eDRAM in-memory computing-based reconfigurable spatial accelerator with triple-mode cell
This article presents DynaPlasia, a reconfigurable eDRAM-based in-memory computing
(IMC) processor with a novel triple-mode cell. It enables higher system-level performance …
(IMC) processor with a novel triple-mode cell. It enables higher system-level performance …