Bio-inspired axonal and dendritic delay-based spiking neural network algorithms perform spatiotemporal pattern recognition efficiently within feed-forward networks, making complex and suboptimal recurrent neural network structures unnecessary. Including trainable dendritic or axonal delays in feed-forward neural networks reduces neural network complexity and improves classification performance significantly. However, generating tunable low-power hardware spike delays of the biological timescale (few μs to ms ) without adding an extra penalty on the area has been challenging over the years. We present a novel band-to-band tunneling-based tunable delay element for spiking neural network hardware. The proposed low-power core delay element capable of providing spike delays of up to 0.4 ms (without explicit capacitance) consumes an area of 50 μm2in GF45RFSOI technology with a peak power of 320 nW, which is the lowest among state-of-the-art spike delay generation circuits. Moreover, the order of the spike delay can be extended to 25 ms by adding an explicit on-chip capacitance of 500 fF.

Combinatorial optimization problems (COPs), when addressed using traditional von Neumann computers, demand significant computational power and substantial area as problem dimensionality increases. Hardware-based solvers, particularly those employing coupled oscillator networks to mimic Ising machines, have been explored as alternatives. However, conventional CMOS-based solutions face limitations in terms of power consumption and area. In this work, we propose a low-power 8-node oscillatory neural network using a band-to-band-tunneling-based ring oscillator in GF45RFSOI technology. This ultra-low power and low-area design efficiently solves COPs without an external perturbation signal. We use the intrinsic noise of BTBT-based oscillators to augment the phase synchronization among the coupled oscillators. The proposed system allows configurable all-to-all connectivity between ring oscillator nodes through cross-coupled capacitors. We demonstrate the system’s efficacy in solving multiple vector graph coloring problems, achieving an average power consumption of 0.93 nW (105× lower than state-of-the-art) per oscillator with a supply voltage of 1.8 V.

On-chip training at the edge becomes a primary requisite for real-time and security-sensitive artificial neural network (ANN) applications. In-memory computation (IMC) techniques have been proposed to facilitate data-intensive computational operations in ANNs. IMC-based multiply-accumulate (MAC) accelerates ANN training but suffers from significant communication overhead between the MAC engine and the off-chip storage for the intermediate data. This article proposes an analog temporary on-chip memory (ATOM) to store this intermediate data during ANN training. The ANN training architecture with the proposed ATOM has two significant advantages. First, the energy required to store intermediate data is scaled down by \(\sim\) 40 \(\times\) due to the on-chip and analog nature of the memory. Second, the proposed architecture avoids power and area-consuming analog-to-digital converters (ADCs) between neural network stages. The ATOM cell measurements are carried out from 20 fabricated chips, and the impact of ATOM characteristics on ANN system performance accuracy is analyzed. This article shows significant latency improvement of \(\sim\) 9 \(\times\) and area savings of \(\sim\) 5 \(\times\) for intermediate data storage compared to the on-chip SRAM during ANN training’s forward and backward pass operations. An improvement in the area and latency will be beneficial to instrument the area- and energy-efficient hardware system for on-chip ANN applications.

Efficient and compact neurons with low power consumption are crucial when designing large-scale spiking neural networks (SNNs) for hardware implementation. Many architectures in the literature showcase different spike patterns associated with biological neurons. However, using bulky capacitors to generate the different time constants related to complex neuron patterns makes these circuits area inefficient. This paper presents a band-to-band-tunneling (BTBT) based energy-efficient and compact neuron capable of producing various spike patterns. The BTBT region’s extremely low current enables different time constants while eliminating the need of bulky capacitors. The circuit is based on the Izhikevich neuron model. The proposed circuit is designed in Silicon on Insulator technology to exhibit important firing patterns observed in the biological cortex, viz. regular spiking, fast-spiking, and chattering, and it is fine-tuned for efficient operation at low subthreshold voltages. This circuit utilizes only 129 μm2area and consumes only 6.7 fJ energy per spike ( approximately 40% lower area and energy per spike than state-of-the-art multi-mode neurons) in G45RFSOI technology.

Solving classification problems using Spiking Neural Networks (SNNs) involves determining the most active neuron in the output layer. Scalable, low-power and low-area hardware solutions for such decision-making are vital for neuromorphic edge applications to meet power and space constraints. In this work, we propose a low-power, compact Winner-Take-All (WTA) circuit, a multi-input multi-output dynamic threshold comparator that simultaneously compares multiple analog voltage inputs and provides a one-hot-encoded digital output vector indicating the result of the classification. The design eliminates the need for cascading and a dedicated feedback circuit. A spike integrator stage captures the temporal activity of a set of neurons, and these activities are compared and digitized by the proposed WTA comparator stage. The proposed WTA designed in GF45RFSOI technology, exhibits self-excitation and global-inhibition properties, offers scalability, consumes 44% less power (140 nW ) and occupies a 40% lower area (166 μm2), compared to state-of-the-art.

This work presents a continuous-time delta-sigma modulator (CTDSM) deploying an experimentally demonstrated band-to-band-tunelling (BTBT) SOI MOSFET-based loop filter. With a compact, low-pass filter circuit and extremely low current in the BTBT regime, a loop filter implementation will provide optimality in terms of area and power performance. In literature for moderate-resolution CTDSMs, traditional loop filters are implemented with either fully passive, active, or hybrid integrators. These designs have a tight tradeoff in terms of area and power. The passive integrators have optimal power but suboptimal area, while the active integrators have optimal area and sub-optimal power consumption. The proposed work tries to break this tradeoff using BTBT regime loop filters. The CTDSM was designed in a GF45RFSOI technology and achieves a peak SNR/SNDR of 48.41 dB/47.94 dB for a 5 kHz bandwidth. The power consumption is 76.3 nW, with an area of 102.7 μm2— more than 100x area reduction over previous state-of-the-art moderate-precision CTDSM designs. This makes the proposed CTDSM extremely compact and power-efficient compared to traditional state-of-the-art moderate-resolution DSMs.

Liquid State Machine (LSM) is a brain-inspired neural network architecture for solving temporal classification problems like speech recognition. The simple structure of LSM with a reservoir and single-layer classifier is attractive from a hardware implementation perspective. When the LSM is considered for low-power hardware implementation in real-world command word recognition tasks, challenges like nonidealities in sensor filter response and ambient noise become critical concerns. In this work, we evaluate the performance of LSM based on two aspects (1) ambient noise and (2) sensor/preprocessing circuit nonidealities. For Ambient noise, we use additive white gaussian noise (AWGN) and ambient noise using the iNoise Indian Noise dataset that covers various natural indoor, outdoor, and travel-related environmental sounds. To understand the impact of input hardware nonidealities, we analyzed the impact of the audio preprocessing filter's quality factor, order, center frequency variations, and output nonlinearity on LSM performance. We use the spoken digits classification in the TI-46 dataset. This paper's findings present design guidelines for the system designers intending to use liquid-state machines for speech classification tasks. In terms of filter design, first, there is a broad Q, order space for filter design where performance is high. We use the hardware-friendly parallel 4th order Butterworth bandpass filter model to provide a baseline 98% accuracy in speech classification tasks. Second, the performance of LSM degrades proportionally to the variation in the center frequency of the bandpass filters in the filter bank. Third, nonlinearity with the third-order harmonic of 50 dBc can be tolerated. Regarding ambient noise, our study shows that a 40 dB SNR for AWGN is sufficient for ideal performance. Second, the best case of “home” noise leads to a performance of 91.4%. Outdoor and travel noise reduce the classification performance to 78.8% and 62.4%, respectively. However, ideal performance is recovered if the signal to noise ratio (SNR) is increased, particularly by 10 dB in indoor conditions and 30 dB in outdoor conditions. Thus, our study presents an engineering evaluation for real-world spoken digit recognition using LSMs.