Embedded - Microprocessors

Part Number	Description / PDF	Quantity
AM3352BZCE30 Texas Instruments	AM3352 SITARA PROCESSOR: ARM COR	220
3530ECBCAMERCURY Texas Instruments	IC PROCESSOR APPL SPEC 515FCBGA	0
AM4372BZDNA60 Texas Instruments	SITARA CORTEX-A9 PROCESSOR	0
AM3354ZCED50 Texas Instruments	32-BIT, 500MHZ, CMOS, PBGA298	397
AM4376BZDND100 Texas Instruments	MOD ARM CORTEX-A9 491NFBGA	0
OMAPL132BZWT2 Texas Instruments	MPU CIRCUIT, CMOS, PBGA361	765
AM5728BABCXEA Texas Instruments	SITARA PROCESSOR	227
OMAP3515ECUS72 Texas Instruments	IC MPU OMAP-35XX 720MHZ 423FCBGA	0
AM3352BZCZA80 Texas Instruments	IC MPU SITARA 800MHZ 324NFBGA	271
AM5726BABCXEA Texas Instruments	SITARA PROCESSOR	18
AM3358BZCE60 Texas Instruments	AM3358BZCE60	0
AM3352BZCZA60 Texas Instruments	IC MPU SITARA 600MHZ 324NFBGA	403
AM1802BZWTD3 Texas Instruments	RISC MCU, 32-BIT, FLASH, 300MHZ	569
AM3352BZCZA30 Texas Instruments	IC MPU SITARA 300MHZ 324NFBGA	115
AM3517AZCNA Texas Instruments	IC MPU SITARA 600MHZ 491NFBGA	82
OMAP3530ECBC72 Texas Instruments	IC MPU OMAP-35XX 720MHZ 515FCBGA	0
AM1707BZKBD4 Texas Instruments	RISC MPU, 32-BIT, 456MHZ	12169
OMAP3515DCUS Texas Instruments	DIGITAL SIGNAL PROCESSOR	270
AM3356BZCZD60 Texas Instruments	IC MPU SITARA 600MHZ 324NFBGA	433
AM1707DZKBA3 Texas Instruments	AM1707 SITARA PROCESSOR: 32-BIT,	1810

Embedded - Microprocessors

1. Overview

Embedded microprocessors are specialized computing units designed for dedicated control, monitoring, or processing tasks within electronic systems. Unlike general-purpose CPUs, they integrate processing cores, memory interfaces, and peripheral controllers into a single chip (SoC) to optimize performance, power efficiency, and cost for specific applications. These devices form the backbone of modern IoT, automotive systems, industrial automation, and consumer electronics.

2. Main Types and Functional Classification

Type	Functional Characteristics	Application Examples
ARM Cortex-M Series	Low-power 32-bit cores with real-time capabilities, optional DSP extensions	Smart sensors, wearables, motor control
PowerPC	High reliability, floating-point units, automotive/Aerospace focused	Vehicle ECUs, avionics systems
MIPS	Efficient pipelining, 32/64-bit variants for multimedia processing	Networking equipment, set-top boxes
RISC-V	Open instruction set, modular architecture for customization	AI accelerators, edge computing devices
x86 (Embedded)	PC compatibility, high processing power with integrated GPUs	Industrial PCs, medical imaging equipment

3. Architecture and Components

A typical embedded microprocessor contains:

Processing Core(s): RISC/Complex Instruction Set architectures with 1-8 cores
Memory Subsystem: Integrated SRAM, ROM, and external DRAM controllers
Peripheral Interfaces: UART, SPI, I2C, USB, CAN, PCIe, Ethernet MAC
Real-Time Components: Watchdog timers, PWM generators, ADC/DAC modules
Power Management: Multiple sleep modes, DVFS (Dynamic Voltage/Frequency Scaling)

4. Key Technical Specifications

Parameter	Description	Importance
Clock Frequency	Operating speed (10MHz-6GHz)	Determines processing throughput
Instruction Set	RISC vs CISC architecture	Affects code density and power consumption
TDP (Thermal Design Power)	Power consumption under load (100mW-50W)	Dictates thermal management requirements
Process Node	Manufacturing technology (28nm-5nm)	Impacts performance and energy efficiency
Memory Bandwidth	Data transfer rate between core and memory	Limits performance in data-intensive tasks

5. Application Domains

Consumer Electronics: Smartphones (application processors), home appliances
Automotive: ADAS controllers, engine management systems
Industrial: PLCs (Programmable Logic Controllers), robotics
Healthcare: MRI scanners, portable diagnostic devices
Communications: 5G base stations, optical network transceivers

6. Leading Manufacturers and Products

Manufacturer	Representative Product	Key Features
ARM Holdings	Cortex-A78	128-bit NEON engine, 4nm process, 3.0GHz
Intel	Atom x6425E	Quad-core, 12W TDP, integrated Intel HD Graphics
NXP Semiconductors	i.MX 8M Plus	1.8GHz Cortex-A53, NPU for ML acceleration
Microchip	SAM9X60	32-bit ARM926EJ-S, 120MHz, ECC memory support
Qualcomm	QCS610	Hexagon DSP, Adreno GPU, AI Engine for computer vision

7. Selection Guidelines

Key considerations include:

Performance requirements vs power budget (e.g., Cortex-M55 for ultra-low-power AI)
Real-time constraints (deterministic latency for motor control applications)
Peripheral integration (CAN FD for automotive networks)
Software ecosystem (RTOS support, middleware availability)
Longevity and supply chain stability (automotive-grade qualification)

Case Study: A smart thermostat design selected NXP's MCIMX7U5 (Cortex-M4 + Cortex-A7) for its combination of real-time sensor processing and application-layer connectivity.

8. Industry Trends

Emerging directions include:

AI integration: On-chip neural processing units (NPUs) for edge ML inference
Heterogeneous computing: Multi-architecture SoCs (RISC-V + GPU + NPU)
Advanced packaging: 3D-stacked memory integration for bandwidth-intensive applications
Functional safety: ISO 26262 compliance for autonomous vehicle systems
Open ecosystems: Growth of RISC-V adoption in custom ASIC designs