design, both inter- and intra-chiplet, impacts overall system performance as well as its manufacturing cost and thermal feasibility. This paper introduces a cross-layer methodology for designing networks in 2.5D systems. We optimize the network design and chiplet placement jointly across logical, physical, and circuit layers to achieve an energy-efficient network, while maximizing system performance, minimizing manufacturing cost, and adhering to thermal constraints. In the logical layer, our co-optimization considers eight different network topologies. In the physical layer, we consider routing, microbump assignment, and microbump pitch constraints to account for the extra costs associated with microbump utilization in the inter-chiplet communication. In the circuit layer, we consider both passive and active links with five different link types, including a gas station link design. Using our cross-layer methodology results in more accurate determination of (superior) inter-chiplet network and 2.5D system designs compared to prior methods. Compared to 2D systems, our approach achieves 29% better performance with the same manufacturing cost, or 25% lower cost with the same performance.
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Chiplet-Package Co-Design For 2.5D Systems Using Standard ASIC CAD Tools
Chiplet integration using 2.5D packaging is gaining popularity nowadays which enables several interesting features like heterogeneous integration and drop-in design method. In the traditional die-by-die approach of designing a 2.5D system, each chiplet is designed independently without any knowledge of the package RDLs. In this paper, we propose a Chip-Package Co-Design flow for implementing 2.5D systems using existing commercial chip design tools. Our flow encompasses 2.5D-aware partitioning suitable for SoC design, Chip-Package Floorplanning, and post-design analysis and verification of the entire 2.5D system. We also designed our own package planners to route RDL layers on top of chiplet layers. We use an ARM Cortex-M0 SoC system to illustrate our flow and compare analysis results with a monolithic 2D implementation of the same system. We also compare two different 2.5D implementations of the same SoC system following the drop-in approach. Alongside the traditional die-by-die approach, our holistic flow enables design efficiency and flexibility with accurate cross-boundary parasitic extraction and design verification.
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- Award ID(s):
- 1755981
- NSF-PAR ID:
- 10166521
- Date Published:
- Journal Name:
- 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC)
- Page Range / eLocation ID:
- 351 to 356
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Neuromorphic computing is an emerging field with the potential to offer performance and energy-efficiency gains over traditional machine learning approaches. Most neuromorphic hardware, however, has been designed with limited concerns to the problem of integrating it with other components in a heterogeneous System-on-Chip (SoC). Building on a state-of-the-art reconfigurable neuromorphic architecture, we present the design of a neuromorphic hardware accelerator equipped with a programmable interface that simplifies both the integration into an SoC and communication with the processor present on the SoC. To optimize the allocation of on-chip resources, we develop an optimizer to restructure existing neuromorphic models for a given hardware architecture, and perform design-space exploration to find highly efficient implementations. We conduct experiments with various FPGA-based prototypes of many-accelerator SoCs, where Linux-based applications running on a RISC-V processor invoke Pareto-optimal implementations of our accelerator alongside third-party accelerators. These experiments demonstrate that our neuromorphic hardware, which is up to 89× faster and 170× more energy efficient after applying our optimizer, can be used in synergy with other accelerators for different application purposes.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ASP-DAC47756.2020.9045734
