Optimizing convolutional neural networks on multi-core vector accelerator

Liu, Zhong; Xiao, Xiangming; Li, Chen; Ma, Shaoping; Deng, Rangyu

doi:10.1016/j.parco.2022.102945

“…Converting the processing latency into seconds requires platform-specific assumptions regarding the required clock cycles per operation, the clock frequency, and a system's capability for parallel processing. For the remainder of this paper, a clock frequency of 1 MHz and 3 MAC operations per clock cycle were assumed [49], to provide a more intuitive comparison of the latency of the evaluated decoders. For simplicity, one addition corresponding to the sparse synaptic operation of neurons in the SNN is assumed to be equivalent to one MAC in terms of required clock cycles.…”

Section: Latencymentioning

confidence: 99%

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Hueber,

Tang,

Sifalakis

et al. 2024

Neuromorph. Comput. Eng.

1

0

View full text Add to dashboard Cite

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge due to the constrained operational environment, requiring low latency and high energy efficiency. Previous benchmarks have provided limited insights into energy efficiency and latency. This paper, however, introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex, and explores the suitability for low latency and low compute neural decoding. The study finds that ANN-based decoders provide superior decoding accuracy, requiring a high latency and many operations to decode neural signals effectively. Spiking neural networks emerge as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of the computational resources. This distinctive advantages of neuromorphic spiking neural networks, positions them as highly suitable for the challenging environment of closed-loop neural modulation. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intracortical human-machine interaction.

show abstract

“…Converting the processing latency into seconds requires platform-specific assumptions regarding the required clock cycles per operation, the clock frequency, and a system's capability for parallel processing. For the remainder of this paper, a clock frequency of 1MHz and 3 MAC operations per clock cycle were assumed 49 . For simplicity, one addition is assumed to be equivalent to one MAC in terms of required clock cycles.…”

Section: B) the Lstm Extracts Spikes With A Temporal Resolution Of 4 ...mentioning

confidence: 99%

Benchmarking of Hardware-efficient Real-time Neural Decoding in Brain-computer Interfaces

Hueber,

Tang,

Sifalakis

et al. 2023

Preprint

0

View full text Add to dashboard Cite

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge due to the constrained operational environment, requiring low latency and high energy efficiency. Previous benchmarks have provided limited insights into energy efficiency and latency. This paper, however, introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex, and explores the suitability for low latency and low compute neural decoding. The study finds that ANN-based decoders provide superior decoding accuracy, requiring a high latency and many operations to decode neural signals effectively. Spiking neural networks emerge as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of the computational resources. These distinctive advantages of neuromorphic spiking neural networks, positions them as highly suitable for the challenging environment of closed-loop neural modulation. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intracortical human-machine interaction.

show abstract

“…However, converting the processing latency into seconds requires platform-specific assumptions about the necessary clock cycles per operation, the clock frequency, and a system's capability to parallel process. For the rest of this paper, a clock frequency of 1MHz and 3 MAC operations per clock cycle are assumed 49 . For simplicity, one single addition is assumed to be equivalent to a MAC operation in terms of required clock cycles.…”

Section: Snnmentioning

confidence: 99%

Benchmarking of Hardware-efficient Real-time Neural Decoding in Brain-computer Interfaces

Hueber,

Tang,

Sifalakis

et al. 2023

Preprint

1

0

View full text Add to dashboard Cite

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge due to the constrained operational environment, requiring low latency and high energy efficiency. Previous benchmarks have provided limited insights into energy efficiency and latency. This paper, however, introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex, and explores the suitability for low latency and low compute neural decoding. The study finds that ANN-based decoders provide superior decoding accuracy, requiring a high latency and many operations to decode neural signals effectively. Spiking neural networks emerge as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of the computational resources. These distinctive advantages of neuromorphic spiking neural networks, positions them as highly suitable for the challenging environment of closed-loop neural modulation. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intracortical human-machine interaction.

show abstract

Optimizing convolutional neural networks on multi-core vector accelerator

Cited by 5 publications

References 4 publications

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Benchmarking of Hardware-efficient Real-time Neural Decoding in Brain-computer Interfaces

Benchmarking of Hardware-efficient Real-time Neural Decoding in Brain-computer Interfaces

Contact Info

Product

Resources

About