Abstract—Increasing investment in computing technologies
and the advancements in silicon technology has fueled rapid
growth in advanced driver assistance systems (ADAS) and
corresponding SoC developments.
An ADAS SoC represents a heterogeneous architecture
that consists of CPUs, GPUs and artificial intelligence (AI)
accelerators. In order to guarantee its safety and reliability,
it must process massive amount of raw data collected from
multiple redundant sources such as high-definition video
cameras, Radars, and Lidars to recognize objects correctly and
to make the right decisions promptly. A domain specific memory
architecture is essential to achieve the above goals.
We present a shared memory architecture that enables high data
throughput among multiple parallel accesses native to the ADAS
applications. It also provides deterministic access latency with
proper isolation under the stringent real-time QoS constraints.
A prototype is built and analyzed. The results validate that the
proposed architecture provides close to 100% throughput for
both read and write accesses generated simultaneously by many
accessing masters with full injection rate. It can also provide
consistent QoS to the domain specific payloads while enabling
the scalability and modularity of the design.
Index Terms—ADAS, Shared Memory, Interconnect, heterogeneous, Many Core SoC
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