Publications de Robert Giterman

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Gain-cell embedded DRAM (GC-eDRAM) is an attractive alternative to conventional SRAM due to its high-density, low-leakage, and inherent two-ported functionality. However, its dynamic storage mechanism requires power-hungry refresh cycles to maintain data. ...

Among the different types of dynamic random-access memories (DRAMs), gain-cell embedded DRAM (GC-eDRAM) is a compact, low-power, and CMOS-compatible alternative to conventional static random-access memory (SRAM). GC-eDRAM achieves high memory density, as i ...

Side-channel attacks constitute a concrete threat to IoT systems-on-a-chip (SoCs). Embedded memories implemented with 6T SRAM macrocells often dominate the area and power consumption of these SoCs. Regardless of the computational platform, the side-channel ...

A gain-cell embedded DRAM (GC-eDRAM) is an attractive logic-compatible alternative to the conventional static random access memory (SRAM) for the implementation of embedded memories, as it offers higher density, lower leakage, and two-ported operation. How ...

Ultra-low power applications often require several kb of embedded memory and are typically operated at the lowest possible operating voltage (VDD) to minimize both dynamic and static power consumption. Embedded memories can easily dominate the overall sili ...

The rise of data-intensive applications has resulted in an increasing demand for high-density and low-power on-chip embedded memories. Gain-cell embedded DRAM (GC-eDRAM) is a logic-compatible alternative to conventional static random access memory (SRAM) w ...

Current variation aware design methodologies, tuned for worst-case scenarios, are becoming increasingly pessimistic from the perspective of power and performance. A good example of such pessimism is setting the refresh rate of DRAMs according to the worst- ...

Among the different types of DRAMs, gain-cell embedded DRAM (GC-eDRAM) is a compact, low-power and CMOS-compatible alternative to conventional SRAM. GC-eDRAM achieves high memory density as it relies on a storage cell that can be implemented with as few as ...

Embedded memories, mostly implemented with static random access memory (SRAM), dominate the area and power of integrated circuits. Gain-cell embedded DRAM (GC-eDRAM) is an alternative to SRAM due to its high density, low power consumption, and two-ported f ...

Gain-cell embedded DRAM (GC-eDRAM) is a logic-compatible embedded memory alternative to SRAM, offering higher density, lower leakage power consumption, and an inherent two-ported functionality. However, increased leakage currents and process variations und ...

Power analysis attacks are an effective tool to extract sensitive information using side-channel analysis, forming a serious threat to IoT systems-on-a-chip (SoCs). Embedded memories implemented with conventional 6T SRAM macrocells often dominate the area ...

Logic-compatible gain-cell embedded DRAM (GC-eDRAM) is an emerging alternative to conventional SRAM for memory-dominated system-on-chip (SoC) designs due to its high-density, low-power, and two-ported operation. Although GCs have a limited data retention t ...

Gain-cell-embedded DRAM (GC-eDRAM) is an attractive alternative to traditional 6T SRAM, as it offers higher density, lower leakage power, and two-ported functionality. However, its refresh requirement also results in power consumption and memory access lim ...

Embedded memories occupy an increasingly dominant part of the area and power budgets of modern systems-on-chips (SoCs). Multi-ported embedded memories, commonly used by media SoCs and graphical processing units, occupy even more area and consume higher pow ...

Gain-cell embedded DRAM (GC-eDRAM) is a high-density logic-compatible alternative to conventional static random-access memory (SRAM) and embedded DRAM (eDRAM). However, GC-eDRAM suffers from a reduced data retention time (DRT) at deeply-scaled process node ...

A fin field-effect transistor (FinFET) based semiconductor memory array having a plurality of memory cells, each memory cell including a write transistor having a write wordline gate over a first fin connected to a write wordline gate contact, a write bitl ...

Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algorithmic description of the network to the hardware implementation. Moreover, ...

Robert Giterman

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GC-eDRAM With Body-Bias Compensated Readout and Error Detection in 28-nm FD-SOI

Gain-Cell Embedded DRAMs: Modeling and Design Space

Low-Cost Side-Channel Secure Standard 6T-SRAM-Based Memory With a 1% Area and Less Than 5% Latency and Power Overheads

Improving Energy-Efficiency in Dynamic Memories Through Retention Failure Detection

Exploration of Sub-VT and Near-VT 2T Gain-Cell Memories for Ultra-Low Power Applications under Technology Scaling

Current-Based Data-Retention-Time Characterization of Gain-Cell Embedded DRAMs Across the Design and Variations Space

Energy versus Data Integrity Trade-Offs in Embedded High-Density Logic Compatible Dynamic Memories

Gain-Cell Embedded DRAMs: Modeling and Design Space

Replica Bit-Line Technique for Internal Refresh in Logic-Compatible Gain-Cell Embedded DRAM

A 1-Mbit Fully Logic-Compatible 3T Gain-Cell Embedded DRAM in 16-nm FinFET

Power Analysis Resilient SRAM Design Implemented with a 1% Area Overhead Impedance Randomization Unit for Security Applications

A 24 kb Single-Well Mixed 3T Gain-Cell eDRAM with Body-Bias in 28 nm FD-SOI for Refresh-Free DSP Applications

64-kB 65-nm GC-eDRAM With Half-Select Support and Parallel Refresh Technique

Multi-Ported GC-eDRAM Bitcell with Dynamic Port Configuration and Refresh Mechanism

GC-eDRAM with Body-Bias Compensated Readout and Error Detection in 28nm FD-SOI

A 128-kbit GC-eDRAM With Negative Boosted Bootstrap Driver for 11.3x Lower-Refresh Frequency at a 2.5% Area Overhead in 28-nm FD-SOI

Fin field-effect transistor (finfet) based semiconductor memory array having memory cells with a reduced surface area

Lupulus: A Flexible Hardware Accelerator For Neural Networks