{"id":342,"date":"2022-02-14T01:51:41","date_gmt":"2022-02-14T01:51:41","guid":{"rendered":"https:\/\/green-ic.org\/CogniVision\/?page_id=342"},"modified":"2022-02-14T09:16:24","modified_gmt":"2022-02-14T09:16:24","slug":"impact","status":"publish","type":"page","link":"https:\/\/green-ic.org\/CogniVision\/impact\/","title":{"rendered":"IMPACT"},"content":{"rendered":"\t\t
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Publications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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1<\/td> S. Jain, L. Lin, M. Alioto, Adaptive Digital Circuits for Power-Performance Range beyond Wide Voltage Scaling, Springer, 2020<\/td>\n<\/tr>\n
2<\/td>A. Alvarez, G. Ponnusamy, M. Alioto, \u201cEnergy-Quality Scalable Memory-Frugal Feature Extraction for Always-On Deep Sub-mW Distributed Vision,\u201d IEEE Access, vol. 8, pp. 18951-18961, Jan. 2020<\/td>\n<\/tr>\n
3<\/td>S. Jain, L. Longyang, M. Alioto, \u201cAutomated Design of Reconfigurable Micro-Architectures for Accelerators under Wide Voltage Scaling,\u201d IEEE Trans. on VLSI Systems, vol. 28, no. 3, pp. 777-790, March 2020<\/td>\n<\/tr>\n
4<\/td>J. H. Teo, S. Cheng, M. Alioto, \u201cLow-Energy Voice Activity Detection via Energy-Quality Scaling from Data Conversion to Machine Learning,\u201d IEEE Trans. on CAS \u2013 part I, vol. 67, no. 4, pp. 1378-1377, April 2020<\/td>\n<\/tr>\n
5<\/td>O. Aiello, P. Crovetti, M. Alioto, \u201cFully Synthesizable Low-Area Analogue-to-Digital Converters with Minimal Design Effort Based on the Dyadic Digital Pulse Modulation,\u201d IEEE Access, vol. 8, pp. 70890-70899, April 2020<\/td>\n<\/tr>\n
6<\/td>S. Jain, L. Lin, M. Alioto, \u201cBroad-Purpose In-Memory Computing for Signal Monitoring and Machine Learning Workloads,\u201d IEEE Solid-State Circuits Letters (invited), vol. 3, pp. 394-397, Sept. 2020<\/td>\n<\/tr>\n
7<\/td>P. Toledo, P. Crovetti, O. Aiello, M. Alioto, \u201cFully Digital Rail-to-Rail OTA with Sub-1,000 \u00b5m2 Area, 250-mV Minimum Supply and nW Power at 150-pF Load in 180nm,\u201d IEEE Solid-State Circuits Letters, vol. 3, pp. 474-477, Sept. 2020<\/td>\n<\/tr>\n
8<\/td>S. Jain, L. Lin, M. Alioto, \u201cProcessor Energy-Performance Range Extension Beyond Voltage Scaling via Drop-In Methodologies,\u201d IEEE Journal of Solid-State Circuits (invited), vol. 55, no. 10, pp. 2670-2679, Oct. 2020<\/td>\n<\/tr>\n
9<\/td>L. Lin, S. Jain, M. Alioto, \u201cSub-nW Microcontroller with Dual-Mode Logic and Self-startup for Battery-Indifferent Sensor Nodes,\u201d accepted to IEEE JSSC<\/td>\n<\/tr>\n
10<\/td>L. Fassio, F. Settino, L. Longyang, R. De Rose, M. Lanuzza, F. Crupi, M. Alioto, \u201cA Robust Sub-Threshold, Low Power-Delay, Energy and Area Efficient Level Shifter,\u201d accepted to IEEE TCAS-II<\/td>\n<\/tr>\n
11<\/td>O. Aiello, P. Crovetti, P. Toledo, M. Alioto, \u201cRail-to-Rail Dynamic Voltage Comparator Scalable down to pW-Range Power and 0.15-V Supply,\u201d accepted to IEEE Trans. on Circuits and Systems \u2013 part II<\/td>\n<\/tr>\n
12<\/td>U. De Alwis, M. Alioto, \u201cTempDiff: Feature Map-Level CNN Sparsity Enhancement at Near-Zero Memory Overhead via Temporal Difference,\u201d IEEE JETCAS, vol. 11, no. 4, pp. 620-633, Dec. 2021<\/td>\n<\/tr>\n
13<\/td>V. Konandur Rajanna, M. Alioto, \u201cOn-Chip Links with Energy-Quality Tradeoff in Error-Resilient and Machine Learning Applications,\u201d accepted to IEEE Journal of Solid-State Circuits<\/td>\n<\/tr>\n
14<\/td>S. Jain, L. Lin, M. Alioto, \u201c\u00b1CIM SRAM for Signed In-Memory Broad-Purpose Computing from DSP to Neural Processing,\u201d accepted to IEEE Journal of Solid-State Circuits (invited)<\/td>\n<\/tr>\n
15<\/td>A Multi-Mode Multi-Coil Coupled Tuned Inductive Peaking ILFD for Low Injected Power With Compact Size<\/td>\n<\/tr>\n
16<\/td>Design of Reconfigurable dB-Linear Variable-Gain Amplifier and Switchable-Order Gm-C Filter in 65-nm CMOS Technology<\/td>\n<\/tr>\n
17<\/td>Design of a wideband variable-gain amplifier with self-compensated transistor for accurate dB-linear characteristic in 65 nm CMOS technology<\/td>\n<\/tr>\n
18<\/td>Design of Differential Variable-Gain Transimpedance Amplifier in 0.18-\u00b5m SiGe BiCMOS<\/td>\n<\/tr>\n
19<\/td>Ka-Band Marchand Balun with Edge and Broadside Coupled Configuration<\/td>\n<\/tr>\n
20<\/td>Design of a Ka-Band U-Shaped Bandpass Filter with 20-GHz Bandwidth in 0.13-\u03bcm BiCMOS Technology<\/td>\n<\/tr>\n
21<\/td>A 60 GHz 8-Way Combined Power Amplifier in 0.18 \u03bcm SiGe BiCMOS<\/td>\n<\/tr>\n
22<\/td>A Two-Stage X-Band 20.7-dBm Power Amplifier in 40-nm CMOS Technology<\/td>\n<\/tr>\n
23<\/td>A Monolithically Integrated Single-Input Load-Modulated Balanced Amplifier with Enhanced Efficiency at Power Back-Off<\/td>\n<\/tr>\n
24<\/td>Neural Epitome Search for Architecture-Agnostic Network Compression<\/td>\n<\/tr>\n
25<\/td>T.-N. Pham, Q.-K. Trinh, I.-J. Chang, M. Alioto, \u201cSTT-BNN: A Novel STT-MRAM In-Memory Computing Macro for Binary Neural Networks,\u201d IEEE JETCAS, vol. 12, no. 2, pp. 569-579, June 2022T.-N. Pham, Q.-K. Trinh, I.-J. Chang, M. Alioto, \u201cSTT-BNN: A Novel STT-MRAM In-Memory Computing Macro for Binary Neural Networks,\u201d IEEE JETCAS, vol. 12, no. 2, pp. 569-579, June 2022<\/td>\n<\/tr>\n
26<\/td>Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach<\/td>\n<\/tr>\n
27<\/td>K. Ali, J. H. Teo, S. Sarkar, M. Alioto, \u201cDual-Mode Conversion Gating, Comparator Merging and Reference-Less Calibration for 2.7X Energy Reduction in SAR ADCs under Low-Activity Inputs,\u201d in print on IEEE Solid-State Circuits Letters<\/td>\n<\/tr>\n
28<\/td>A Linear-in-Decibel Automatic Gain Control Amplifier With Dual Mode Continuous Gain Tuning<\/td>\n<\/tr>\n
29<\/td>A Fully Integrated Pulling Mitigation Synthesizer for NB-IoT Transmitter<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Presentations<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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1<\/td>S. Jain, L. Longyang, M. Alioto, \u201cLow-Overhead Drop-In Techniques to Extend the Energy-Performance Tradeoff in Microcontrollers Beyond VDD Scaling,\u201d in Proc. of ASSCC 2019, pp. 125-129, Macau (China), Nov. 2019<\/td>\n<\/tr>\n
2<\/td>An inductorless 6-GHz variable gain differential transimpedance amplifier in 0.18-\u03bcm SiGe BiCMOS<\/td>\n<\/tr>\n
3<\/td>Design of a Voltage-Controlled Programmable-Gain Amplifier in 65-nm CMOS Technology<\/td>\n<\/tr>\n
4<\/td>An Inductorless 5-GHz Differential Dual Regulated Cross-Cascode Transimpedance Amplifier using 40 nm CMOS<\/td>\n<\/tr>\n
5<\/td>O. Aiello, P. Crovetti, A. Sharma, M. Alioto, \u201cFully-Synthesizable Current-Input ADCs for Ultra-Low Area and Minimal Design Effort,\u201d accepted to ICECS 2019<\/td>\n<\/tr>\n
6<\/td>Rethinking Bottleneck Structure for Efficient Mobile Network Design<\/td>\n<\/tr>\n
7<\/td>Ultra-Low Power Receiver Architecture with Enhanced Input Signal Swing for Improved Sensitivity<\/td>\n<\/tr>\n
8<\/td>An Ultra-Low Power 900 MHz Intermediate Frequency Low Noise Amplifier for Low-Power RF Receivers<\/td>\n<\/tr>\n
9<\/td>CMOS Transformer Design for X-band Power Amplifier Applications<\/td>\n<\/tr>\n
10<\/td>S. Jain, L. Lin, M. Alioto, \u201cBroad-Purpose In-Memory Computing for Signal Monitoring and Machine Learning Workloads Based on Commercial Bitcell,\u201d accepted to IEEE ASSCC 2020 (invited and accepted to IEEE SSC-L)<\/td>\n<\/tr>\n
11<\/td>J. H. Teo, K. Ali, M. Alioto, \u201cVoice Activity Detection with >83% Accuracy under SNR down to -3dB at 1.19\u00b5W and 0.07mm2 in 40nm,\u201d accepted to IEEE ASSCC 2020<\/td>\n<\/tr>\n
12<\/td>U. De Alwis, M. Alioto, \u201cTempDiff: Temporal Difference-Based Feature Map-Level Sparsity Induction in CNNs with <4% Memory Overhead,\u201d accepted to IEEE AICAS 2021<\/td>\n<\/tr>\n
13<\/td>P. Leite Correia, P. Crovetti, H. Klimach, S. Bampi, O. Aiello, M. Alioto, \u201cA 300mV-Supply, sub-nW-Power Digital-Based Operational Transconductance Amplifier,\u201d accepted to IEEE AICAS 2021<\/td>\n<\/tr>\n
14<\/td>V. Konandur Rajanna, S. Taneja, M. Alioto, \u201cA 109TOPS\/mm2 and 749-1,459TOPS\/W SRAM Buffer with In-Memory Inference and Prediction-Less Bitline Activity Reduction in 28nm,\u201d accepted to IEEE ESSCIRC 2021<\/td>\n<\/tr>\n
15<\/td>S. Wu, K. De Silva, S. Gutgutia, B. Baas, M. Alioto, \u201cA 1448-Mpixel\/s, 84-pJ\/pixel Display Stream Compression Encoder in 28 nm for 4K Video Resolution,\u201d accepted to IEEE ASSCC 2021<\/td>\n<\/tr>\n
16<\/td>Ku-Band Bidirectional Mixer with Directional Control<\/td>\n<\/tr>\n
17<\/td>A 60 GHz Edge-Coupled 4-Way Balun Power Amplifier with 22.7dBm Output Power and 27.7% Peak Efficiency<\/td>\n<\/tr>\n
18<\/td>A 24.6-32.5 GHz Millimeter-wave Frequency Synthesizer for 5G Wireless and 60 GHz Applications<\/td>\n<\/tr>\n
19<\/td>A New Degeneration Technique for Low-voltage, Low-power, Triple-cascode Low Noise Amplifier in Millimeter Wave Applications<\/td>\n<\/tr>\n
20<\/td>A. Gupta, V. Konandur, T. Salam, S. Jain, O. Aiello, P. Crovetti, M. Alioto, \u201cDDPMnet: All-Digital Pulse Density-Based DNN Architecture with 228 Gate Equivalents\/MAC Unit, 28-TOPS\/W and 1.5-TOPS\/mm2 in 40nm,\u201d accepted to IEEE CICC 2022<\/td>\n<\/tr>\n
21<\/td>U. De Alwis, M. Alioto, \u201cTemporal Redundancy-Based Computation Reduction for 3D Convolutional Neural Networks,\u201d accepted to IEEE AICAS 2022<\/td>\n<\/tr>\n
22<\/td>A Hybrid Pulling Mitigation Synthesizer for NB-IoT Transmitter<\/td>\n<\/tr>\n
23<\/td>Ultra-Low Power 2.4 GHz Receiver Design Techniques for IoT Applications<\/td>\n<\/tr>\n
24<\/td>K. A. Ahmed, H. Okuhara, M. Alioto, \u201c55-pW\/pixel Peak Power Imager with Near-Sensor Novelty\/Edge Detection and DC-DC Converter-Less MPPT for Purely-Harvested Sensor Nodes,\u201d in IEEE ISSCC Dig. Tech. Papers, Feb. 2022, pp. 102-103<\/td>\n<\/tr>\n
25<\/td>U. De Alwis, X. Zhongheng, M. Alioto, \u201cTemporal Similarity-Based Computation Reduction for Video Transformers in Edge Camera Nodes,\u201d accepted to IEEE AICAS 2023<\/td>\n<\/tr>\n
26<\/td>A. Gupta, S. Kumar, V. Konandur, S. Taneja, M. Alioto, \u201cVisual Content-Agnostic Novelty Detection Engine with 2.4 pJ\/pixel Energy and Two-Order of Magnitude DNN Activity Reduction in 40 nm,\u201d in Proc. of VLSI Symposium 2023, Kyoto (Japan), June 2023<\/td>\n<\/tr>\n
27<\/td>Kehong Gong, Dongze Lian, Heng Chang, Chuan Guo, Zihang Jiang, Xinxin Zuo, Michael Bi Mi, Xinchao Wang, “TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration”<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Demos and public materials<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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RECMICRO: complete design framework to design reconfigurable microarchitectures<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Talks<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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1<\/td>Neural Epitome Search for Architecture-Agnostic Network Compression, Presentation at ICLR conference on 25\/04\/2020<\/td>\n<\/tr>\n
2<\/td>Survival of the fittest: circuits and architectures for computation with ultra-wide power-performance adaptation beyond voltage scaling \u2013 keynote speech at the IEEE S3S 2019 conference, Oct 16, 2019, San Jose (USA)<\/a><\/td>\n<\/tr>\n
3<\/td>Survival of the fittest: circuits and architectures with wide power-performance adaptation \u2013 Beyond voltage scaling and down to pWs \u2013 keynote speech at the IEEE MCSoC 2019 conference, Oct 1, 2019, Singapore<\/a><\/td>\n<\/tr>\n
4<\/td> Survival of the fittest: circuits and architectures with wide power-performance adaptation beyond voltage scaling \u2013 keynote speech at the IEEE SOCC 2019 conference, Sept 3, 2019, Singapore<\/a><\/td>\n<\/tr>\n
5<\/td>Energy-Quality Scalable Integrated Systems – Preserving Energy Downscaling in the Decade Ahead \u2013 keynote speech at the IEEE SigTelCom 2019 conference, March 20, 2019, Hanoi (Vietnam)<\/a><\/td>\n<\/tr>\n
6<\/td>Widely-Adaptive Intelligent & Connected Systems for Nearly-Unstoppable Operation under Highly Uncertain Power Availability<\/a><\/td>\n<\/tr>\n
7<\/td>From Less Batteries to Battery-Less SoCs with Nearly-Unstoppable Operation \u2013 Towards a Greener and Smarter World \u2013 keynote speech at the IEEE ISOCC 2020 conference, Oct. 22, 2020, Yeosu (Korea)<\/a><\/td>\n<\/tr>\n
8<\/td>Always-On Sensor Nodes Entirely Powered by Sustainable Energy Sources \u2013 Enabling a Smarter and Greener World \u2013 keynote speech at the IEEE iSES 2020 conference, Dec 14, 2020, Chennai (India)<\/a><\/td>\n<\/tr>\n
9<\/td>Widely-Adaptive Intelligent&Connected Systems for Nearly-Unstoppable Operation under Highly Uncertain Power Availability \u2013 keynote speech at the IEEE ICCE 2021 conference, Jan 13, 2021, Phu Quoc Island (Vietnam)<\/a><\/td>\n<\/tr>\n
10<\/td>Always-On Sensor Nodes Entirely Powered by Sustainable Energy Sources \u2013 Enabling a Smarter and Greener World \u2013 keynote speech at the Renewable Energy and Power Engineering conference, Nov 9-11, 2021, Beijing<\/a><\/td>\n<\/tr>\n
11<\/td>Intelligent Systems with Ultra-Wide Power-Performance Adaptation – Going Well beyond the Diminishing Returns of Voltage Scaling, keynote speech at IEEE APCCAS and Prime Asia (Penang, Malaysia), Nov 22-26, 2021<\/a><\/td>\n<\/tr>\n
12<\/td>2019 International Workshop on Advanced Chip Microsystems<\/td>\n<\/tr>\n
13<\/td>13th IEEE International Conference on ASIC (ASICON 2019)<\/td>\n<\/tr>\n
14<\/td>Circuits and Highly Energy-Efficient Processing for Sensor Data Sensemaking: from Near-Threshold Circuits to On-Chip Deep Learning<\/td>\n<\/tr>\n
15<\/td>2020 International Workshop on Microwave and Microsystems<\/td>\n<\/tr>\n
16<\/td>Circuits and Architectures for Next-Generation Attentive & Intelligent Systems<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Publications Presentations Demos and public materials RECMICRO: complete design framework to design reconfigurable microarchitectures Talks<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"nf_dc_page":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-342","page","type-page","status-publish","hentry"],"featured_image_src":null,"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/green-ic.org\/CogniVision\/wp-json\/wp\/v2\/pages\/342","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/green-ic.org\/CogniVision\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/green-ic.org\/CogniVision\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/green-ic.org\/CogniVision\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/green-ic.org\/CogniVision\/wp-json\/wp\/v2\/comments?post=342"}],"version-history":[{"count":5,"href":"https:\/\/green-ic.org\/CogniVision\/wp-json\/wp\/v2\/pages\/342\/revisions"}],"predecessor-version":[{"id":479,"href":"https:\/\/green-ic.org\/CogniVision\/wp-json\/wp\/v2\/pages\/342\/revisions\/479"}],"wp:attachment":[{"href":"https:\/\/green-ic.org\/CogniVision\/wp-json\/wp\/v2\/media?parent=342"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}