The semiconductor industry is steadily shifting toward disaggregated system design to overcome the scalability (yield and cost) limitations of monolithic integration. 2.5D integration offers a compelling pathway for realizing such systems, enabling the assembly of heterogeneous chiplets—compute, memory, analog & I/O, with dense interconnects and high bandwidth. Our previous work showcased experimental results from a multi-foundry chiplet design over a large passive silicon base. There were 20 chip slots (CS) on an interposer that can be configured with compute die (CD) or memory die (MD) chiplets. Building on this foundation, this paper introduces a methodology for system technology co-optimization (STCO) across several vectors. These include varying the number of chip slots, configuring slots with different numbers of MDs and CDs, sweeping the inter-die bandwidth, choosing between different technology nodes for the performance limiting MDs. This work demonstrates how the design and technology choices affect system performance, power, and cost across different workloads, empowering designers to select optimal configurations for their specific needs.

Chiplet-based architectures and advanced packaging have emerged as transformative approaches in semiconductor design. While conventional physical design for 2.5D heterogeneous systems typically prioritizes wirelength reduction through tight chiplet packing, this strategy introduces thermal bottlenecks and intensifies coefficient of thermal expansion (CTE) mismatches, compromising long-term reliability. Addressing these challenges requires holistic consideration of thermal performance, mechanical stress, and interconnect efficiency. We introduce STAMP-2.5D: Structural and Thermal Aware Methodology for Placement in 2.5D integration, the first automated placement methodology that simultaneously optimizes these critical factors. Our approach employs finite element analysis to simulate temperature distributions and stress profiles across chiplet configurations while minimizing interconnect wirelength. Experimental results demonstrate that a thermal and structurally aware automated placement approach reduces overall stress by 11 %, maintains excellent thermal performance with a negligible$\mathbf{0. 5 \%}$temperature increase and simultaneously reduces total wirelength by 11 % compared to temperature-only optimization. Additionally, we conduct an exploratory study on the effects of temperature gradients on structural integrity, providing crucial insights for reliability-conscious chiplet design.

This work identifies the architectural and design scaling limits of 2-D flexible interconnect deep neural network (DNN) accelerators and addresses them with 3-D ICs. We demonstrate how scaling up a baseline 2-D accelerator in the$X/Y$dimension fails and how vertical stacking effectively overcomes the failure. We designed multitier accelerators that are$1.67\times $faster than the 2-D design. Using our 3-D architecture and circuit codesign methodology, we improve throughput, energy efficiency, and area efficiency by up to$5\times $,$1.2\times $, and$3.9\times $, respectively, over 2-D counterparts. The IR-drop in our 3-D designs is within 10.7% of VDD, and the temperature variation is within 12 °C.

Motion Estimation (ME) is a major bottleneck of a Video encoding pipeline. This paper presents a low power near memory Sum of Absolute Difference (SAD) accelerator for ME. The accelerator is composed of 64 modular SAD Processing Elements (PEs) on a Reconfigurable fabric, offering maximal parallelism to support traditional and futuristic Rate-Distortion-Optimization (RDO) schemes consistent with HEVC/AV1/JEM. The accelerator offers up-to 55% speedup over State-of-art accelerators and a 7x speedup when compared to a 12 core Intel Xeon E5 processor. Our solution achieves 93 TOPS/Watt running at 500MHz frequency, capable of processing real-time 4K 30fps video. Synthesized in 22nm process, the accelerator occupies 0.08mm2 and consumes 5.46mW dynamic power.

Processing-in-memory (PIM) architectures attempt to overcome the von Neumann bottleneck by combining computation and storage logic into a single component. The content-addressable parallel processing paradigm (CAPP) from the seventies is an in-situ PIM architecture that leverages content-addressable memories to realize bit-serial arithmetic and logic operations, via sequences of search and update operations over multiple memory rows in parallel. In this paper, we set out to investigate whether the concepts behind classic CAPP can be used successfully to build an entirely CMOS-based, general-purpose microarchitecture that can deliver manyfold speedups while remaining highly programmable. We conduct a full-stack design of a Content-Addressable Processing Engine (CAPE), built out of dense push-rule 6T SRAM arrays. CAPE is programmable using the RISC-V ISA with standard vector extensions. Our experiments show that CAPE achieves an average speedup of 14 (up to 254) over an area-equivalent (slightly under 9 mm2at 7 nm) out-of-order processor core with three levels of caches.

This paper presents a Look-Up Table (LUT) based Processing-In-Memory (PIM) technique with the potential for running Neural Network inference tasks. We implement a bitline computing free technique to avoid frequent bitline accesses to the cache sub-arrays and thereby considerably reducing the memory access energy overhead. LUT in conjunction with the compute engines enables sub-array level parallelism while executing complex operations through data lookup which otherwise requires multiple cycles. Sub-array level parallelism and systolic input data flow ensure data movement to be confined to the SRAM slice.Our proposed LUT based PIM methodology exploits substantial parallelism using look-up tables, which does not alter the memory structure/organization, that is, preserving the bit-cell and peripherals of the existing SRAM monolithic arrays. Our solution achieves 1.72× higher performance and 3.14x lower energy as compared to a state-of-the-art processing-in-cache solution. Sub-array level design modifications to incorporate LUT along with the compute engines will increase the overall cache area by 5.6%. We achieve 3.97x speedup w.r.t neural network systolic accelerator with a similar area. The re-configurable nature of the compute engines enables various neural network operations and thereby supporting sequential networks (RNNs) and transformer models. Our quantitative analysis demonstrates 101×, 3× faster execution and 91×, 11× energy efficient than CPU and GPU respectively while running the transformer model, BERT-Base.

In-memory computing has emerged as a promising solution to address the logic-memory performance gap. We propose design techniques using monolithic-3D integration to achieve reliable multirow activation which in turn help in computation as part of data readout. Our design is 1.8x faster than the existing techniques for Boolean computations. We quantitatively show no impact to cell stability when multiple rows are activated and thereby requiring no extra hardware for maintaining the cell stability during computations. In-memory digital to analog conversion technique is proposed using a 3D-CAM primitive. The design utilizes relatively low strength layer-2 transistors effectively and provides 7x power savings when compared with a specialized converter in-memory. Lastly, we present a linear classifier system by making use the above-mentioned techniques which is 47x faster while computing vector matrix multiplication using a dedicated hardware engine.

Several emerging reconfigurable technologies have been explored in recent years offering device level runtime reconfigurability. These technologies offer the freedom to choose between p- and n-type functionality from a single transistor. In order to optimally utilize the feature-sets of these technologies, circuit designs and storage elements require novel design to complement the existing and future electronic requirements. An important aspect to sustain such endeavors is to supplement the existing design flow from the device level to the circuit level. This should be backed by a thorough evaluation so as to ascertain the feasibility of such explorations. Additionally, since these technologies offer runtime reconfigurability and often encapsulate more than one functions, hardware security features like polymorphic logic gates and on-chip key storage come naturally cheap with circuits based on these reconfigurable technologies. This paper presents innovative approaches devised for circuit designs harnessing the reconfigurable features of these nanotechnologies. New circuit design paradigms based on these nano devices will be discussed to brainstorm on exciting avenues for novel computing elements.

We present a novel 3D-SRAM cell using a Monolithic 3D integration (M3D-IC) technology for realizing both robustness and In-memory Boolean logic compute support. The proposed two-layer design makes use of additional transistors over the SRAM layer to enable assist techniques as well as provide logic functions (such as AND/NAND, OR/NOR, XNOR/XOR) without degrading cell density. Through analysis, we provide insights into the benefits provided by three memory assist and two logic modes and evaluate the energy efficiency of our proposed design. Assist techniques improve SRAM read stability by 2.2x and increase the write margin by 17.6%, while staying within the SRAM footprint. By virtue of increased robustness, the cell enables seamless operation at lower supply voltages and thereby ensures energy efficiency. Energy Delay Product (EDP) reduces by 1.6x over standard 6T SRAM with a faster data access. Transistor placement and their biasing technique in layer-2 enables In-memory bitwise Boolean computation. When computing bulk In-memory operations, 6.5x energy savings is achieved as compared to computing outside the memory system.

This paper proposes the use of monolithic 3-D integration technology in designing a novel two-layer 3-D-static random access memory (3-D-SRAM) cell in standard 6T-SRAM footprint. The proposed 3-D-SRAM cell is capable of data access from both the layers. The cell is designed to retrieve row-wise and column-wise data concurrently from the memory array. This memory design can cater to applications and workloads requiring multidimensional data access for enhancing system performance. The novel 3-D layout technique ensures the same footprint as a 6T-SRAM cell despite enhancing the functionality. The design ensures no degradation in the cell stability and performance. Voltage reduction in layer-2 provides 5.4× power savings during column-wise data access. We analyze the implications of employing the proposed SRAM to achieve efficient data access for integral image algorithm. We obtain 2.15× savings in access time and 7.81% access energy savings while accessing data from a 32-kB memory array to compute integral image for a region of 32 rows and 16 columns.

Batteryless energy harvesting systems face a twofold challenge in converting incoming energy into forward progress. Not only must such systems contend with inherently weak and fluctuating power sources, but they have very limited temporal windows for capitalizing on transitory periods of above-average power. To maximize forward progress, such systems should aggressively consume energy when it is available, rather than optimizing for peak averagecase efficiency. However, there are multiple ways that a processor can trade between consumption and performance. In this paper, we examine two approaches, frequency scaling and resource scaling, and develop a predictor-driven scheme for dynamically allocating future power budgets between the two techniques. We show that our solution can achieve forward progress equal to 2.08X of the baseline Out-of-Order (OoO) processor with the best static configuration of frequency and resources. The combined technique outperforms either technique in isolation, with frequency-only and resource-only approaches achieving 1.43X and 1.61X forward progress improvements, respectively.