7 research outputs found
Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
We present the BrainScaleS-2 mobile system as a compact analog inference
engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at
classifying a medical electrocardiogram dataset. The analog network core of the
ASIC is utilized to perform the multiply-accumulate operations of a
convolutional deep neural network. At a system power consumption of 5.6W, we
measure a total energy consumption of 192uJ for the ASIC and achieve a
classification time of 276us per electrocardiographic patient sample. Patients
with atrial fibrillation are correctly identified with a detection rate of
(93.70.7)% at (14.01.0)% false positives. The system is directly
applicable to edge inference applications due to its small size, power
envelope, and flexible I/O capabilities. It has enabled the BrainScaleS-2 ASIC
to be operated reliably outside a specialized lab setting. In future
applications, the system allows for a combination of conventional machine
learning layers with online learning in spiking neural networks on a single
neuromorphic platform
Inference with Artificial Neural Networks on Analog Neuromorphic Hardware
The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and
synapse circuits as well as two versatile digital microprocessors. Primarily
designed to emulate spiking neural networks, the system can also operate in a
vector-matrix multiplication and accumulation mode for artificial neural
networks. Analog multiplication is carried out in the synapse circuits, while
the results are accumulated on the neurons' membrane capacitors. Designed as an
analog, in-memory computing device, it promises high energy efficiency.
Fixed-pattern noise and trial-to-trial variations, however, require the
implemented networks to cope with a certain level of perturbations. Further
limitations are imposed by the digital resolution of the input values (5 bit),
matrix weights (6 bit) and resulting neuron activations (8 bit). In this paper,
we discuss BrainScaleS-2 as an analog inference accelerator and present
calibration as well as optimization strategies, highlighting the advantages of
training with hardware in the loop. Among other benchmarks, we classify the
MNIST handwritten digits dataset using a two-dimensional convolution and two
dense layers. We reach 98.0% test accuracy, closely matching the performance of
the same network evaluated in software
The BrainScaleS-2 Neuromorphic Platform — A Report on the Integration and Operation of an Open Science Hardware Platform within EBRAINS
This report presents the challenges encountered and the solutions created for the operation of the BrainScaleS neuromorphic platform, and the overall progress leading to this state at the end of the Human Brain Project (HBP)
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability, and efficiency
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
We present first experimental results on the novel BrainScaleS-2 neuromorphic
architecture based on an analog neuro-synaptic core and augmented by embedded
microprocessors for complex plasticity and experiment control. The high
acceleration factor of 1000 compared to biological dynamics enables the
execution of computationally expensive tasks, by allowing the fast emulation of
long-duration experiments or rapid iteration over many consecutive trials. The
flexibility of our architecture is demonstrated in a suite of five distinct
experiments, which emphasize different aspects of the BrainScaleS-2 system