Analog Vector-Matrix Multiplier Based on Programmable Current Mirrors for Neural Network Integrated Circuits

We propose a CMOS Analog Vector-Matrix Multiplier for Deep Neural Networks, implemented in a standard single-poly 180 nm CMOS technology. The learning weights are stored in analog floating-gate memory cells embedded in current mirrors implementing the multiplication operations. We experimentally verify the analog storage capability of designed single-poly floating-gate cells, the accuracy of the multiplying function of proposed tunable current mirrors, and the effective number of bits of the analog operation. We perform system-level simulations to show that an analog deep neural network based on the proposed vector-matrix multiplier can achieve an inference accuracy comparable to digital solutions with an energy efficiency of 26.4 TOPs/J, a layer latency close to 100 mu s and an intrinsically high degree of parallelism. Our proposed design has also a cost advantage, considering that it can be implemented in a standard single-poly CMOS process flow

Ring-oscillator with multiple transconductors for linear analog-to-digital conversion

This paper proposes a new circuit-based approach to mitigate nonlinearity in open-loop ring-oscillator-based analog-to-digital converters (ADCs). The approach consists of driving a current-controlled oscillator (CCO) with several transconductors connected in parallel with different bias conditions. The current injected into the oscillator can then be properly sized to linearize the oscillator, performing the inverse current-to-frequency function. To evaluate the approach, a circuit example has been designed in a 65-nm CMOS process, leading to a more than 3-ENOB enhancement in simulation for a high-swing differential input voltage signal of 800-mVpp, with considerable less complex design and lower power and expected area in comparison to state-of-the-art circuit based solutions. The architecture has also been checked against PVT and mismatch variations, proving to be highly robust, requiring only very simple calibration techniques. The solution is especially suitable for high-bandwidth (tens of MHz) medium-resolution applications (10–12 ENOBs), such as 5G or Internet-of-Things (IoT) devices.This research was funded by Project TEC2017-82653-R, Spain

Spiking LCA in a Neural Circuit with Dictionary Learning and Synaptic Normalization

The Locally Competitive Algorithm (LCA) [17, 18] was put forward as a model of primary visual cortex [14, 17] and has been used extensively as a sparse coding algorithm for multivariate data. LCA has seen implementations on neuromorphic processors, including IBM’s TrueNorth processor [10], and Intel’s neuromorphic research processor, Loihi, which show that it can be very efficient with respect to the power resources it consumes [8]. When combined with dictionary learning [13], the LCA algorithm encounters synaptic instability [24], where, as a synapse’s strength grows, its activity increases, further enhancing synaptic strength, leading to a runaway condition, where synapses become saturated [3, 15]. A number of approaches have been suggested to stabilize this phenomenon [1, 2, 5, 7, 12]. Previous work demonstrated that, by extending the cost function used to generate LCA updates, synaptic normalization could be achieved, eliminating synaptic runaway [7]. It was also shown that the resulting algorithm could be implemented in a firing rate model [7]. Here, we implement a probabilistic approximation to this firing rate model as a spiking LCA algorithm that includes dictionary learning and synaptic normalization. The algorithm is based on a synfire-gated synfire chain-based information control network in concert with Hebbian synapses [16, 19]. We show that this algorithm results in correct classification on numeric data taken from the MNIST datase

Single-poly floating-gate memory cell options for analog neural networks

In this paper, we explore the use of a 180 nm CMOS single-poly technology platform for realizing analog Deep Neural Network integrated circuits. The analysis focuses on analog vector–matrix multiplier architectures, one of the main building blocks of a neural network, implementing in-memory computation using Floating-Gate multi-level non-volatile memories. We present two memory options, suited either for current-mode or for time-domain vector–matrix multiplier implementations, with low–voltage charge-injection program and erase operations. The effects of a limited accuracy are also investigated through system-level simulations, by accounting for the temperature dependence of the stored weights and the corresponding impact on the network error rate

Subthreshold swing model using scale length for sub-10 nm junction-based double-gate MOSFETs

We propose an analytical model for subthreshold swing using scale length for sub-10 nm double gate (DG) MOSFETs. When the order of the calculation for the series type potential distribution is increased it is possible to obtain accuracy, but there is a problem that the calculation becomes large. Using only the first order calculation of potential distribution, we derive the scale length λ1 and use it to obtain an analytical model of subthreshold swing. The findings show this subthreshold swing model is in concordance with a 2D simulation. The relationship between the channel length and silicon thickness, which can analyze the subthreshold swing using λ1, is derived by the relationship between the scale length and the geometric mean of the silicon and oxide thickness. If the silicon thickness and oxide film thickness satisfy the condition of (Lg-0.215)/6.38 > tsi(=tox), it is found that the result of this model agrees with the results using higher order calculations, within a 4% error range

SPICE model of drain induced barrier lowering in sub-10 nm junctionless cylindrical surrounding gate MOSFET

We propose a SPICE Drain Induced Barrier Lowering (DIBL) model for sub-10 nm Junctionless Cylindrical Surrounding Gate (JLCSG) MOSFETs. The DIBL shows the proportionl relation to the -3 power of the channel length Lg and the 2 power of silicon thickness in MOSFET having a rectangular channel, but this relation cannot be used in cylindrical channel because of the difference in channel structure. The subthreshold currents, including the tunneling current from the WKB (Wentzel-Kramers-Brillouin) approximation as well as the diffusion-drift current, are used in the model. The constant current method is used to define the threshold voltage as the gate voltage at a constant current, (2πR/Lg)10-7 A for channel length and channel radius R. The central potential of the JLCSG MOSFET is determined by the Poisson equation. As a result, it can be seen that the DIBL of the JLCSG MOSFET is proportional to the –2.76 power of the channel length, to the 1.76 power of the channel radius, and linearly to the oxide film thickness. At this time, we observe that the SPICE parameter, the static feedback coefficient, has a value less than 1 1, and this model can be used to analyze the DIBL of the JLCSG MOSFET

A Survey on Integrated Circuit Trojans

Traditionally, computer security has been associated with the software security, or the information-data security. Surprisingly, the hardware on which the software executes or the information stored-processed-transmitted has been assumed to be a trusted base of security. The main building blocks of any electronic device are Integrated circuits (ICs) which form the fabric of a computer system. Lately, the use of ICs has expanded from handheld calculators and personal computers (PCs) to smartphones, servers, and Internet-of-Things (IoT) devices. However, this significant growth in the IC market created intense competition among IC vendors, leading to new trends in IC manufacturing. System-on-chip (SoC) design based on intellectual property (IP), a globally spread supply chain of production and distribution of ICs are the foremost of these trends. The emerging trends have resulted in many security and trust weaknesses and vulnerabilities, in computer systems. This includes Hardware Trojans attacks, side-channel attacks, Reverse-engineering, IP piracy, IC counterfeiting, micro probing, physical tampering, and acquisition of private or valuable assets by debugging and testing. IC security and trust vulnerabilities may cause loss of private information, modified/altered functions, which may cause a great economical hazard and big damage to society. Thus, it is crucial to examine the security and trust threats existing in the IC lifecycle and build defense mechanisms against IC Trojan threats. In this article, we examine the IC supply chain and define the possible IC Trojan threats for the parties involved. Then we survey the latest progress of research in the area of countermeasures against the IC Trojan attacks and discuss the challenges and expectations in this area. Keywords: IC supply chain, IC security, IP privacy, hardware trojans, IC trojans DOI: 10.7176/CEIS/12-2-01 Publication date: April 30th 202

Ultra-Low-Power Sensors and Receivers for IoT Applications

The combination of ultra-low power analog front-ends and CMOS-compatible transducers enable new applications, such as environmental monitors, household appliances, health trackers, etc. that are seamlessly integrated into our daily lives. Furthermore, wireless connectivity allows many of these sensors to operate both independently and collectively. These techniques collectively fulfil the recent surge of internet-of-things (IoT) applications that have the potential to fundamentally change daily life for millions of people.In this dissertation, the circuit and system design of wireless receivers and sensors is presented that explores the challenges of implementing long lifespan, high accuracy, and large coverage range IoT sensor networks. The first is a wake-up receiver (WuRX), which continuously monitors the RF environment to wake up a higher-power radio upon detection of a predetermined RF signature. This work both improves sensitivity and reduces power over prior art through a multi-faceted design featuring an impedance transformation network with large passive voltage gain, an active envelope detector with high input impedance to facilitate large passive voltage gain, a low-power precision comparator, and a low-leakage digital baseband correlator.Although pushing the prior WuRX performance boundary by orders of magnitude, the first work shows moderate sensitivity, inferior temperature robustness, and large area with external lumped components. Thus, the second work shows a miniaturized WuRX that is temperature-compensated, yet still consumes only nano-watt power and millimeter area while operating at 9 GHz. To further reduce the area, a global common-mode feedback is utilized across the envelope detector and baseband amplifier that eliminates the need for off-chip ac-coupling components. Multiple temperature-compensation techniques are proposed to maintain constant bandwidth of the signal path and constant clock frequency. Both WuRXs operate at 0.4 V supply, consume near-zero power and achieve ~-70 dBm sensitivity.Lastly, the first reported CMOS 2-in-1 relative humidity and temperature sensor is presented. A unified analog front-end interfaces on-chip transducers and converts the inputs into a frequency vis a high-linearity frequency-locked loop. An incomplete-settling switched-capacitor-based Wheatstone bridge is proposed to sense the inputs in a power-efficient fashion

Performance Analysis of a 3D Wireless Massively Parallel Computer

In previous work, the authors presented a 3D hexagonal wireless direct-interconnect network for a massively parallel computer, with a focus on analysing processor utilisation. In this study, we consider the characteristics of such an architecture in terms of link utilisation and power consumption. We have applied a store-and-forward packet-switching algorithm to both our proposed architecture and a traditional wired 5D direct network (the same as IBM’s Blue Gene). Simulations show that for small and medium-size networks the link utility of the proposed architecture is comparable with (and in some cases even better than) traditional 5D networks. This work demonstrates that there is a potential for wireless processing array concepts to address High-Performance Computing (HPC) challenges whilst alleviating some significant physical construction drawbacks of traditional systems